Relevant bibliographies by topics / Surrogate experiments

Academic literature on the topic 'Surrogate experiments'

Author: Grafiati
Published: 18 May 2022

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Surrogate experiments":

1

Holssaple, K. A. "Hypervelocity impact experiments in surrogate materials." International Journal of Impact Engineering 14, no. 1-4 (January 1993): 335–45. http://dx.doi.org/10.1016/0734-743x(93)90032-3.

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THOMAS, CHEVISE L., HARSHAVARDHAN THIPPAREDDI, SANJAY KUMAR, MACC RIGDON, ROBERT W. McKEE, and ALEXANDER M. STELZLENI. "Validation of Commonly Used Antimicrobial Interventions on Bob Veal Carcasses for Reducing Shiga Toxin–Producing Escherichia coli Surrogate Populations." Journal of Food Protection 84, no. 7 (February 9, 2021): 1114–21. http://dx.doi.org/10.4315/jfp-20-458.

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ABSTRACT Ruminants are natural reservoirs of Shiga toxin–producing Escherichia coli (STEC), and the STEC can be easily transferred to carcasses during the conversion of animals to meat. Three experiments were conducted to validate the efficacy of lactic acid (LA; 4%), peroxyacetic acid (PAA; 300 ppm), and hot water (HW; 80°C) for their individual or combined abilities to reduce STEC surrogates on bob veal carcasses pre- and postchill and through fabrication. In experiment 1, hot carcasses (n = 9) were inoculated with a five-strain cocktail (ca. 8 log CFU/mL) containing rifampin-resistant surrogate E. coli (BAA-1427, BAA-1428, BAA-1429, BAA-1430, and BAA-1431) and then treated with HW, LA, or PAA. Carcasses were then chilled (0 ± 1°C; 24 h) and split in half, and each side was treated with either LA or PAA. In experiment 2, hot carcasses (n = 3) were inoculated and chilled (24 h). After 24 h, the carcasses were split, and each side was treated with either LA or PAA. For experiment 3, carcasses (n = 3) were chilled for 24 h, split, inoculated, and treated with either LA or PAA. After chilling, carcasses from all three experiments were fabricated to subprimals and the cut surfaces were sampled to determine the translocation of bacteria. Experiment 1 showed that LA+LA was the most effective (P ≤ 0.05) treatment for reducing surrogate E. coli on veal. In experiments 2 and 3, LA and PAA were similar (P > 0.05) in their abilities to reduce E. coli on chilled veal carcasses. In experiments 1 and 2, all antimicrobial treatments resulted in undetectable levels (<0.2 log CFU/cm2) of surrogate E. coli on cut surfaces after fabrication, whereas low levels (1.7 and 1.0 log CFU/cm2 for LA and PAA, respectively) were observed in experiment 3. Of the antimicrobial interventions utilized, LA was more effective for reducing STEC surrogate populations on veal carcasses, pre- and/or postchill. HIGHLIGHTS
3

Muharam, Yuswan, Danny Leonardi, and Alisya P. Ramadhania. "Simulation of the Oxidation and Combustion of Mixed Diesel-Biodiesel Fuel." MATEC Web of Conferences 156 (2018): 03008. http://dx.doi.org/10.1051/matecconf/201815603008.

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A comparative simulation-based research has been set up to obtain valid kinetic models of the oxidation and combustion of biodiesel surrogate and diesel surrogate, as well as mixed diesel-biodiesel surrogates which is used to predict their ignition delay times (IDT). The research consists of the development of the detailed kinetic models of the oxidation and combustion of biodiesel surrogate and diesel surrogate, the validation of the two models with the corresponding experimental IDT data, the merging and the validation of the two models for mixed diesel-biodiesel surrogates. The biodiesel surrogate kinetic model was validated with the experimental IDT data of methyl 9-decenoate at 20 atm and three equivalence ratios. The diesel surrogate kinetic model was validated with the experimental IDT data of n-hexadecane at the pressure ranging from 2 atm to 5 atm and the equivalence ratio of 1.0. The diesel-biodiesel surrogate kinetic model was validated with the experimental IDT data of real diesel-biodiesel fuels for four compositions and at 1.18 atm. The validation results of all models show that the models and the experiments are in good agreement.
4

Adams-Selin, Rebecca D., Adam J. Clark, Christopher J. Melick, Scott R. Dembek, Israel L. Jirak, and Conrad L. Ziegler. "Evolution of WRF-HAILCAST during the 2014–16 NOAA/Hazardous Weather Testbed Spring Forecasting Experiments." Weather and Forecasting 34, no. 1 (January 4, 2019): 61–79. http://dx.doi.org/10.1175/waf-d-18-0024.1.

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Abstract Four different versions of the HAILCAST hail model have been tested as part of the 2014–16 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments. HAILCAST was run as part of the National Severe Storms Laboratory (NSSL) WRF Ensemble during 2014–16 and the Community Leveraged Unified Ensemble (CLUE) in 2016. Objective verification using the Multi-Radar Multi-Sensor maximum expected size of hail (MRMS MESH) product was conducted using both object-based and neighborhood grid-based verification. Subjective verification and feedback was provided by HWT participants. Hourly maximum storm surrogate fields at a variety of thresholds and Storm Prediction Center (SPC) convective outlooks were also evaluated for comparison. HAILCAST was found to improve with each version due to feedback from the 2014–16 HWTs. The 2016 version of HAILCAST was equivalent to or exceeded the skill of the tested storm surrogates across a variety of thresholds. The post-2016 version of HAILCAST was found to improve 50-mm hail forecasts through object-based verification, but 25-mm hail forecasting ability declined as measured through neighborhood grid-based verification. The skill of the storm surrogate fields varied widely as the threshold values used to determine hail size were varied. HAILCAST was found not to require such tuning, as it produced consistent results even when used across different model configurations and horizontal grid spacings. Additionally, different storm surrogate fields performed at varying levels of skill when forecasting 25- versus 50-mm hail, hinting at the different convective modes typically associated with small versus large sizes of hail. HAILCAST was able to match results relatively consistently with the best-performing storm surrogate field across multiple hail size thresholds.
5

Myšáková, Eva, and Matěj Lepš. "Surrogate Based Evaluation of the Design of Experiments." Advanced Materials Research 1144 (March 2017): 148–52. http://dx.doi.org/10.4028/www.scientific.net/amr.1144.148.

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Meta-modeling also known as Surrogate Modeling is one of the commonly used tools foranalysis of complex systems' behavior. The meta-model is constructed based on training data whichconsist of the training points generated via Design of Experiments (DoE) and responses of the originalmodel evaluated in these training points. The positioning of the points is crucial for the approximationquality of the meta-model. Therefore it is appropriate to assess the DoE's quality not only usingthe common geometrical or statistical criteria but also from the point of view of its actual particularpurpose. Such testing is able to recognize the appropriate set of training points and also the possibleability of the individual meta-models for actual problem.
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Melby, Jeffrey A., Fatima Diop, Norberto Nadal-Caraballo, Alex Taflanidis, and Victor Gonzalez. "HURRICANE WATER LEVEL PREDICTION USING SURROGATE MODELING." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 57. http://dx.doi.org/10.9753/icce.v36.currents.57.

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For this study, the surrogate was constructed using kriging (Jia et al. 2015). The high fidelity coupled surge and wave numerical modelling for the Gulf of Mexico was used as the training set. The numerical model was either ADCIRC and STWAVE or ADCIRC and SWAN in the nearshore. The surrogate models were trained using tropical storm parameters (latitude, longitude, central pressure, radius to maximum wind speed, storm heading, and forward speed) at a specific location as inputs and individual responses (e.g. surge) as outputs. Tide was computed separately using ADCIRC and linearly superimposed with surge to get total water level. The regional surrogates accurately reproduced both peaks and time series of water levels for historical storms. An extensive validation was conducted to determine the optimal application of the kriging approach. In this paper we will report the efficient design-of-experiments approach, surrogate training and validation.
7

Bauer, Benedikt, Felix Heimrich, Michael Kohler, and Adam Krzyżak. "On estimation of surrogate models for multivariate computer experiments." Annals of the Institute of Statistical Mathematics 71, no. 1 (November 2, 2017): 107–36. http://dx.doi.org/10.1007/s10463-017-0627-8.

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Tuo, Rui, C. F. Jeff Wu, and Dan Yu. "Surrogate Modeling of Computer Experiments With Different Mesh Densities." Technometrics 56, no. 3 (July 3, 2014): 372–80. http://dx.doi.org/10.1080/00401706.2013.842935.

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Latimer, G. D., W. R. Marcum, and W. F. Jones. "Dispersion of Surrogate LWR Fuel Experiments Under LOCA Conditions." Nuclear Technology 206, no. 9 (March 2, 2020): 1374–84. http://dx.doi.org/10.1080/00295450.2020.1712158.

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Liu, Bolin, and Liyang Xie. "Reliability Analysis of Structures by Iterative Improved Ensemble of Surrogate Method." Shock and Vibration 2019 (October 24, 2019): 1–13. http://dx.doi.org/10.1155/2019/6357104.

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Surrogate models have been widely adopted for reliability analysis. The common approach is to construct a series of surrogates based on a training set and then pick out the best one with the highest accuracy as an approximation of the time-consuming limit state function. However, the traditional method increases the risk of adopting an inappropriate model and does not take full advantage of the data devoted to constructing different surrogates. Furthermore, obtaining more samples is very expensive and sometimes even impossible. Therefore, to save the cost of constructing the surrogate and improve the prediction accuracy, an ensemble strategy is proposed in this paper for efficiently analyzing the structural reliability. The values of the weights are obtained by a recursive process and the leave-one-out technique, in which the values are updated in each iteration until a given prediction accuracy is achieved. Besides, a learning function is used to guide the selection of the next sampling candidate. Because the learning function utilizes the uncertainty estimator of the surrogate to guide the design of experiments (DoE), to accurately calculate the uncertainty estimator of the ensemble of surrogates, the concept of weighted mean square error is proposed. After the high-quality ensemble of surrogates of the limit state function is available, the Monte Carlo method is employed to calculate the failure probabilities. The proposed method is evaluated by three analytic problems and one engineering problem. The results show that the proposed ensemble of surrogates has better prediction accuracy and robustness than the stand-alone surrogates and the existing ensemble techniques.
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You might also be interested in the extended bibliographies on the topic 'Surrogate experiments' for particular source types:
Journal articles Dissertations / Theses

Dissertations / Theses on the topic "Surrogate experiments":

1

Weise, Peter Carl. "Mission-Integrated Synthesis/Design Optimization of Aerospace Subsystems under Transient Conditions." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/76855.

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The equations governing the thermodynamic behavior of a military aircraft have been implemented by the Air Force Research Lab (AFRL) and other Integrated Vehicle Energy Technology Demonstration (INVENT) contributors into a cohesive, adaptable, dynamic aircraft simulation program in Mathworks' Simulink®. The resulting model known as the "Tip-to-tail" model meets the design specifications set forth by the INVENT program. The system consists of six intimately linked subsystems that include a propulsion subsystem (PS), air vehicle subsystem (AVS), robust electrical power subsystem (REPS), high power electric actuation subsystem (HPEAS), advanced power and thermal management subsystem (APTMS), and a fuel thermal management subsystem (FTMS). The model's governing equations are augmented with experimental data and supported by defined physical parameters. In order to address the problems associated with the additional power and thermal loads for in more electric aircraft (MEA), this research utilizes exergy analysis and mission-integrated synthesis/design optimization to investigate the potential for improvement in tip-to-tail design/performance. Additionally, this thesis describes the development and integration of higher fidelity transient heat exchanger models for use in the tip-to-tail. Finally, the change in performance due to the integration of new heat exchanger models developed here is presented. Additionally, this thesis discusses the results obtained by performing mission-integrated synthesis/design optimization on the tip-to-tail using heat exchanger design parameters as decision variables. These results show that the performance of the tip-to-thermal management subsystems improves significantly due to the integration of the heat exchanger models. These results also show improvements in vehicle performance due to the mission-integrated optimization.
Master of Science
2

Boopathy, Komahan. "Uncertainty Quantification and Optimization Under Uncertainty Using Surrogate Models." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398302731.

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Bilicz, Sandor. "Application of Design-of-Experiment Methods and Surrogate Models in Electromagnetic Nondestructive Evaluation." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00601753.

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Le contrôle non destructif électromagnétique (CNDE) est appliqué dans des domaines variés pour l'exploration de défauts cachés affectant des structures. De façon générale, le principe peut se poser en ces termes : un objet inconnu perturbe un milieu hôte donné et illuminé par un signal électromagnétique connu, et la réponse est mesurée sur un ou plusieurs récepteurs de positions connues. Cette réponse contient des informations sur les paramètres électromagnétiques et géométriques des objets recherchés et toute la difficulté du problème traité ici consiste à extraire ces informations du signal obtenu. Plus connu sous le nom de " problèmes inverses ", ces travaux s'appuient sur une résolution appropriée des équations de Maxwell. Au " problème inverse " est souvent associé le " problème direct " complémentaire, qui consiste à déterminer le champ électromagnétique perturbé connaissant l'ensemble des paramètres géométriques et électromagnétiques de la configuration, défaut inclus. En pratique, cela est effectué via une modélisation mathématique et des méthodes numériques permettant la résolution numérique de tels problèmes. Les simulateurs correspondants sont capables de fournir une grande précision sur les résultats mais à un coût numérique important. Sachant que la résolution d'un problème inverse exige souvent un grand nombre de résolution de problèmes directs successifs, cela rend l'inversion très exigeante en termes de temps de calcul et de ressources informatiques. Pour surmonter ces challenges, les " modèles de substitution " qui imitent le modèle exact peuvent être une solution alternative intéressante. Une manière de construire de tels modèles de substitution est d'effectuer un certain nombre de simulations exactes et puis d'approximer le modèle en se basant sur les données obtenues. Le choix des simulations (" prototypes ") est normalement contrôlé par une stratégie tirée des outils de méthodes de " plans d'expérience numérique ". Dans cette thèse, l'utilisation des techniques de modélisation de substitution et de plans d'expérience numérique dans le cadre d'applications en CNDE est examinée. Trois approches indépendantes sont présentées en détail : une méthode d'inversion basée sur l'optimisation d'une fonction objectif et deux approches plus générales pour construire des modèles de substitution en utilisant des échantillonnages adaptatifs. Les approches proposées dans le cadre de cette thèse sont appliquées sur des exemples en CNDE par courants de Foucault
4

Friedman, Alex Matthew. "An Approach to Incorporate Additive Manufacturing and Rapid Prototype Testing for Aircraft Conceptual Design to Improve MDO Effectiveness." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/73656.

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The primary objectives of this work are two-fold. First, additive manufacturing (AM) and rapid prototype (RP) testing are evaluated for use in production of a wind tunnel (WT) models. Second, an approach was developed to incorporate stability and control (SandC) WT data into aircraft conceptual design multidisciplinary design optimization (MDO). Both objectives are evaluated in terms of data quality, time, and cost. FDM(TM) and PolyJet AM processes were used for model production at low cost and time. Several models from a representative tailless configuration, ICE 101, were printed and evaluated for strength, cost and time of production. Furthermore, a NACA 0012 model with 20% chord flap was manufactured. Both models were tested in the Virginia Tech (VT) Open-Jet WT for force and moment acquisition. A 1/15th scale ICE 101 model was prepared for manufacturing, but limits of FDM(TM) technology were identified for production. An approach using WT data was adapted from traditional surrogate-based optimization (SBO), which uses computational fluid dynamics (CFD) for data generation. Split-plot experimental designs were developed for analysis of the WT SBO strategy using historical data and for WT testing of the NACA 0012. Limitations of the VT Open-Jet WT resulted in a process that was not fully effective for a MDO environment. However, resolution of ICE 101 AM challenges and higher quality data from a closed-section WT should result in a fully effective approach to incorporate AM and RP testing in an aircraft conceptual design MDO.
Master of Science
5

Qian, Zhiguang. "Computer experiments [electronic resource] : design, modeling and integration /." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11480.

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The use of computer modeling is fast increasing in almost every scientific, engineering and business arena. This dissertation investigates some challenging issues in design, modeling and analysis of computer experiments, which will consist of four major parts. In the first part, a new approach is developed to combine data from approximate and detailed simulations to build a surrogate model based on some stochastic models. In the second part, we propose some Bayesian hierarchical Gaussian process models to integrate data from different types of experiments. The third part concerns the development of latent variable models for computer experiments with multivariate response with application to data center temperature modeling. The last chapter is devoted to the development of nested space-filling designs for multiple experiments with different levels of accuracy.
6

Nixon, Janel Nicole. "A Systematic Process for Adaptive Concept Exploration." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/13952.

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This thesis presents a method for streamlining the process of obtaining and interpreting quantitative data for the purpose of creating a low-fidelity modeling and simulation environment. By providing a more efficient means for obtaining such information, quantitative analyses become much more practical for decision-making in the very early stages of design, where traditionally, quants are viewed as too expensive and cumbersome for concept evaluation. The method developed to address this need uses a Systematic Process for Adaptive Concept Exploration (SPACE). In the SPACE method, design space exploration occurs in a sequential fashion; as data is acquired, the sampling scheme adapts to the specific problem at hand. Previously gathered data is used to make inferences about the nature of the problem so that future samples can be taken from the more interesting portions of the design space. Furthermore, the SPACE method identifies those analyses that have significant impacts on the relationships being modeled, so that effort can be focused on acquiring only the most pertinent information. The results show that the combination of a tailored data set, and an informed model structure work together to provide a meaningful quantitative representation of the system while relying on only a small amount of resources to generate that information. In comparison to more traditional modeling and simulation approaches, the SPACE method provides a more accurate representation of the system using fewer resources to generate that representation. For this reason, the SPACE method acts as an enabler for decision making in the very early design stages, where the desire is to base design decisions on quantitative information while not wasting valuable resources obtaining unnecessary high fidelity information about all the candidate solutions. Thus, the approach enables concept selection to be based on parametric, quantitative data so that informed, unbiased decisions can be made.
7

Thomas, George L. "Biogeography-Based Optimization of a Variable Camshaft Timing System." Cleveland State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=csu1419775790.

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8

Zhang, Boya. "Computer Experimental Design for Gaussian Process Surrogates." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99886.

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With a rapid development of computing power, computer experiments have gained popularity in various scientific fields, like cosmology, ecology and engineering. However, some computer experiments for complex processes are still computationally demanding. A surrogate model or emulator, is often employed as a fast substitute for the simulator. Meanwhile, a common challenge in computer experiments and related fields is to efficiently explore the input space using a small number of samples, i.e., the experimental design problem. This dissertation focuses on the design problem under Gaussian process surrogates. The first work demonstrates empirically that space-filling designs disappoint when the model hyperparameterization is unknown, and must be estimated from data observed at the chosen design sites. A purely random design is shown to be superior to higher-powered alternatives in many cases. Thereafter, a new family of distance-based designs are proposed and their superior performance is illustrated in both static (one-shot design) and sequential settings. The second contribution is motivated by an agent-based model(ABM) of delta smelt conservation. The ABM is developed to assist in a study of delta smelt life cycles and to understand sensitivities to myriad natural variables and human interventions. However, the input space is high-dimensional, running the simulator is time-consuming, and its outputs change nonlinearly in both mean and variance. A batch sequential design scheme is proposed, generalizing one-at-a-time variance-based active learning, as a means of keeping multi-core cluster nodes fully engaged with expensive runs. The acquisition strategy is carefully engineered to favor selection of replicates which boost statistical and computational efficiencies. Design performance is illustrated on a range of toy examples before embarking on a smelt simulation campaign and downstream high-fidelity input sensitivity analysis.
Doctor of Philosophy
With a rapid development of computing power, computer experiments have gained popularity in various scientific fields, like cosmology, ecology and engineering. However, some computer experiments for complex processes are still computationally demanding. Thus, a statistical model built upon input-output observations, i.e., a so-called surrogate model or emulator, is needed as a fast substitute for the simulator. Design of experiments, i.e., how to select samples from the input space under budget constraints, is also worth studying. This dissertation focuses on the design problem under Gaussian process (GP) surrogates. The first work demonstrates empirically that commonly-used space-filling designs disappoint when the model hyperparameterization is unknown, and must be estimated from data observed at the chosen design sites. Thereafter, a new family of distance-based designs are proposed and their superior performance is illustrated in both static (design points are allocated at one shot) and sequential settings (data are sampled sequentially). The second contribution is motivated by a stochastic computer simulator of delta smelt conservation. This simulator is developed to assist in a study of delta smelt life cycles and to understand sensitivities to myriad natural variables and human interventions. However, the input space is high-dimensional, running the simulator is time-consuming, and its outputs change nonlinearly in both mean and variance. An innovative batch sequential design method is proposed, generalizing one-at-a-time sequential design to one-batch-at-a-time scheme with the goal of parallel computing. The criterion for subsequent data acquisition is carefully engineered to favor selection of replicates which boost statistical and computational efficiencies. The design performance is illustrated on a range of toy examples before embarking on a smelt simulation campaign and downstream input sensitivity analysis.
9

Feng, Chunyao Seaman John Weldon. "Bayesian evaluation of surrogate endpoints." Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/4187.

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Ormandy, Shannon L. "An Experimental Demonstration of the Surrogate Conditioned Motivating Operation." Thesis, The Chicago School of Professional Psychology, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10750075.

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The present study attempted to establish a flashing or solid red light as a surrogate conditioned motivating operation (CMO-S) by pairing the light with the unconditioned motivating operation (UMO) of heat. The effects of the red light were assessed by an ABAB reversal design for three participants and an ABA reversal design for one participant. Baseline sessions consisted of presenting the red light in the absence of the UMO of heat to assess for any behavioral effects of the red light before and after pairing with the UMO of heat. Galvanic Skin Response (GSR) was recorded for all participants as an indirect measure of sweating. Additional dependent variables and the ambient temperature for each participant’s UMO of heat were identified through a temperature and response assessment. The additional dependent variable was drinking water for Participants 2–4 and throat clearing for Participant 1. Results suggest that the flashing red light may have been conditioned as a CMO-S for Participants 2 and 3. Participant 4 did not consume any water during any baseline session, suggesting that the solid red light did not function as a CMO-S after pairing. Results for Participant 1 suggests that throat clearing was controlled by additional unknown variables and was not evoked by the UMO of heat. Results potentially supporting the CMO-S should be interpreted cautiously given study limitations involving temperature control and the extent that the UMO of heat was clearly demonstrated for each participant.

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Books on the topic "Surrogate experiments":

1

Weisberg, Michael. Modeling. Edited by Herman Cappelen, Tamar Szabó Gendler, and John Hawthorne. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199668779.013.26.

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This article focuses on the methodology of modeling and how it can be applied to philosophical questions. It looks at various traditional views of modeling and defends the idea that modeling is a form of surrogate reasoning involving two distinct steps: indirect representation of a target system using a model and analysis of that model. The article considers different accounts of model/target representational relations, defending an account of similarity. It concludes by presenting several examples of the use of models in philosophy, suggestions for philosophers new to modeling, and an assessment of the relationship between thought experiments and models.
2

Massimini, Marcello, and Giulio Tononi. Assessing Consciousness in Other Humans: From Theory to Practice. Translated by Frances Anderson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198728443.003.0007.

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This chapter translates the theoretical principles illustrated in Chapter 5 into an empirical measure that can be applied to real human brains. It explains how transcranial magnetic stimulation (TMS) and simultaneous electroencephalography (EEG) can be employed to derive a surrogate measure of information integration, the perturbational complexity index (PCI). By describing the results of a series of experiments, it demonstrates that PCI can discriminate with very high accuracy between consciousness and unconsciousness, across many different conditions, ranging from wakefulness to sleep, dreaming esthesia and coma patients. The chapter ends by suggesting that principled measures of brain complexity can also help understanding the mechanisms of loss and recovery of consciousness in both physiological and pathological conditions.
3

Siminoff, Laura A., and Maria D. Thomson. The ethics of communication in cancer and palliative care. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198736134.003.0005.

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Our knowledge and understanding of bioethics is informed by cancer communication research, particularly around the informed consent process and participation in clinical trials. This chapter provides an overview of several ethical theories used to guide bioethics, including principlism, casuistry, and virtue ethics. With these in mind, the informed consent process and the importance of quality communication is described. Despite many patients preferring a style of communication that is more participatory in nature, physicians still struggle with how much and what kind of information to share regarding clinical trials. In the context of palliative care, surrogate decision-making adds an additional layer of complexity to this process. Further, many patients still endorse the ‘therapeutic misconception’, meaning they believe experimental treatments are superior to standard treatments. The communication models of paternalism, the fully informed model, and shared decision-making are described, with the goal to provide patients with clear, unbiased information to inform decision-making.
4

Cain, Andrew. Jerome's Commentaries on the Pauline Epistles and the Architecture of Exegetical Authority. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192847195.001.0001.

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In the late fourth and early fifth centuries, during a fifty-year stretch sometimes dubbed a Pauline “renaissance” of the western church, six different authors produced over four dozen commentaries in Latin on Paul’s epistles. Among them was Jerome, who commented on four epistles (Galatians, Ephesians, Titus, Philemon) in 386 after recently having relocated to Bethlehem from Rome. His commentaries occupy a time-honored place in the centuries-long tradition of Latin-language commenting on Paul’s writings. They also constitute his first foray into the systematic exposition of whole biblical books (and his only experiment with Pauline interpretation on this scale), and so they provide precious insight into his intellectual development at a critical stage of his early career before he would go on to become the most prolific biblical scholar of Late Antiquity. This monograph provides the first book-length treatment of Jerome’s opus Paulinum in any language. Adopting a cross-disciplinary approach, Cain comprehensively analyzes the commentaries’ most salient aspects—from the inner workings of Jerome’s philological method and engagement with his Greek exegetical sources, to his recruitment of Paul as an anachronistic surrogate for his own theological and ascetic special interests. One of the overarching concerns of this book is to explore and to answer, from multiple vantage points, a question that was absolutely fundamental to Jerome in his fourth-century context: what are the sophisticated mechanisms by which he legitimized himself as a Pauline commentator, not only on his own terms but also vis-à-vis contemporary western commentators?
5

Luyckx, Valerie A. Nephron numbers and hyperfiltration as drivers of progression. Edited by David J. Goldsmith. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0138.

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The relationship between low birth weight (LBW) and subsequent increased risk of hypertension and renal disease in humans is now well established. The initial hypothesis suggested that an adverse intrauterine environment, reflected by LBW, would impact renal development, resulting in a low nephron number and predisposition to hypertension and renal disease. Studies in various populations have shown a direct correlation between birth weight and nephron number, and in infants, nephron numbers are reduced in those of LBW. Among Caucasian and Australian Aboriginal adults, lower nephron numbers are associated with higher blood pressure, whereas higher nephron numbers appear to protect against hypertension. LBW is currently the best clinical surrogate for low nephron number and has been independently associated with higher blood pressure from infancy through to adulthood in many populations, as well as an increased risk of proteinuria, reduced glomerular filtration rate, chronic kidney disease, and end-stage renal disease in later life. The pathophysiology is analogous to that in other chronic kidney diseases where surviving nephrons are subject to hyperfiltration early on, resulting in glomerular hypertrophy, proteinuria, and eventually, especially in the setting of other renal disease risk factors, glomerulosclerosis, and loss of renal function. Mean nephron number varies by up to 13-fold in certain populations, however, therefore nephron number is unlikely the sole developmentally programmed risk factor for renal disease in later life, but may be a first ‘hit’ impacting an individual’s susceptibility to or resistance to superimposed renal injury. Augmentation of nephron number perinatally has only been addressed in experimental settings. In humans, therefore optimization of nephron number is likely best achieved through good perinatal care and adequate postnatal nutrition. Cardiovascular disease and diabetes are also developmentally programmed and therefore likely coexist in subjects with LBW and low nephron numbers. Awareness of an individual’s birth weight should serve to highlight the possibility of low nephron number and potential risk for future hypertension and renal disease, which may be attenuated by optimization of early nutrition, lifestyle choices, and management of other risk factors for renal disease.

Book chapters on the topic "Surrogate experiments":

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Pourmohamad, Tony, and Herbert K. H. Lee. "Surrogate Models." In Bayesian Optimization with Application to Computer Experiments, 19–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82458-7_2.

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Pourmohamad, Tony, and Herbert K. H. Lee. "Surrogate Models." In Bayesian Optimization with Application to Computer Experiments, 19–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82458-7_2.

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Diestmann, Thomas, Nils Broedling, Benedict Götz, and Tobias Melz. "Surrogate Model-Based Uncertainty Quantification for a Helical Gear Pair." In Lecture Notes in Mechanical Engineering, 191–207. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_16.

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AbstractCompetitive industrial transmission systems must perform most efficiently with reference to complex requirements and conflicting key performance indicators. This design challenge translates into a high-dimensional multi-objective optimization problem that requires complex algorithms and evaluation of computationally expensive simulations to predict physical system behavior and design robustness. Crucial for the design decision-making process is the characterization, ranking, and quantification of relevant sources of uncertainties. However, due to the strict time limits of product development loops, the overall computational burden of uncertainty quantification (UQ) may even drive state-of-the-art parallel computing resources to their limits. Efficient machine learning (ML) tools and techniques emphasizing high-fidelity simulation data-driven training will play a fundamental role in enabling UQ in the early-stage development phase.This investigation surveys UQ methods with a focus on noise, vibration, and harshness (NVH) characteristics of transmission systems. Quasi-static 3D contact dynamic simulations are performed to evaluate the static transmission error (TE) of meshing gear pairs under different loading and boundary conditions. TE indicates NVH excitation and is typically used as an objective function in the early-stage design process. The limited system size allows large-scale design of experiments (DoE) and enables numerical studies of various UQ sampling and modeling techniques where the design parameters are treated as random variables associated with tolerances from manufacturing and assembly processes. The model accuracy of generalized polynomial chaos expansion (gPC) and Gaussian process regression (GPR) is evaluated and compared. The results of the methods are discussed to conclude efficient and scalable solution procedures for robust design optimization.
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Banton, Rohan, Thuvan Piehler, Nicole Zander, Richard Benjamin, and Josh Duckworth. "Comparison of Numerical Simulations with Experiments of Blast-Induced Pressure Wave Impact on a Surrogate Head Model." In Dynamic Behavior of Materials, Volume 1, 181–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62956-8_30.

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Brazdil, Pavel, Jan N. van Rijn, Carlos Soares, and Joaquin Vanschoren. "Metalearning for Hyperparameter Optimization." In Metalearning, 103–22. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_6.

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SummaryThis chapter describes various approaches for the hyperparameter optimization (HPO) and combined algorithm selection and hyperparameter optimization problems (CASH). It starts by presenting some basic hyperparameter optimization methods, including grid search, random search, racing strategies, successive halving and hyperband. Next, it discusses Bayesian optimization, a technique that learns from the observed performance of previously tried hyperparameter settings on the current task. This knowledge is used to build a meta-model (surrogate model) that can be used to predict which unseen configurations may work better on that task. This part includes the description sequential model-based optimization (SMBO). This chapter also covers metalearning techniques that extend the previously discussed optimization techniques with the ability to transfer knowledge across tasks. This includes techniques such as warm-starting the search, or transferring previously learned meta-models that were trained on prior (similar) tasks. A key question here is how to establish how similar prior tasks are to the new task. This can be done on the basis of past experiments, but can also exploit the information gained from recent experiments on the target task. This chapter presents an overview of some recent methods proposed in this area.
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Vermeeren, Günter, Wout Joseph, and Luc Martens. "Surrogate Modeling for Fast Experimental Assessment of Specific Absorption Rate." In Uncertainty Modeling for Engineering Applications, 71–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04870-9_5.

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Wang, Hong, and Jy-An John Wang. "Experimental Study on Surrogate Nuclear Fuel Rods Under Reversed Cyclic Bending." In Fatigue and Fracture Test Planning, Test Data Acquisitions and Analysis, 19–36. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International, 2017. http://dx.doi.org/10.1520/stp159820160051.

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Sengezer, Engin C., and Gary D. Seidel. "In-Situ Sensing of Deformation and Damage in Nanocomposite Bonded Surrogate Energetic Materials." In Conference Proceedings of the Society for Experimental Mechanics Series, 193–201. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41766-0_23.

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Wilson, Cara C., Thomas Tueting, Debora Ma, Cathy Haluszczak, Michael Lotze, and Walter Storkus. "Activation of Dendritic Cells by Surrogate T Cell Interactions Leads to Enhanced Costimulation, Secretion of TH1-Associated Cytokines, and CTL Inductive Capacity." In Advances in Experimental Medicine and Biology, 335–43. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9966-8_55.

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Serai, Suraj D., and Meng Yin. "MR Elastography of the Abdomen: Basic Concepts." In Methods in Molecular Biology, 301–23. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_18.

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AbstractMagnetic resonance elastography (MRE) is an emerging imaging modality that maps the elastic properties of tissue such as the shear modulus. It allows for noninvasive assessment of stiffness, which is a surrogate for fibrosis. MRE has been shown to accurately distinguish absent or low stage fibrosis from high stage fibrosis, primarily in the liver. Like other elasticity imaging modalities, it follows the general steps of elastography: (1) apply a known cyclic mechanical vibration to the tissue; (2) measure the internal tissue displacements caused by the mechanical wave using magnetic resonance phase encoding method; and (3) infer the mechanical properties from the measured mechanical response (displacement), by generating a simplified displacement map. The generated map is called an elastogram.While the key interest of MRE has traditionally been in its application to liver, where in humans it is FDA approved and commercially available for clinical use to noninvasively assess degree of fibrosis, this is an area of active research and there are novel upcoming applications in brain, kidney, pancreas, spleen, heart, lungs, and so on. A detailed review of all the efforts is beyond the scope of this chapter, but a few specific examples are provided. Recent application of MRE for noninvasive evaluation of renal fibrosis has great potential for noninvasive assessment in patients with chronic kidney diseases. Development and applications of MRE in preclinical models is necessary primarily to validate the measurement against “gold-standard” invasive methods, to better understand physiology and pathophysiology, and to evaluate novel interventions. Application of MRE acquisitions in preclinical settings involves challenges in terms of available hardware, logistics, and data acquisition. This chapter will introduce the concepts of MRE and provide some illustrative applications.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction chapter is complemented by another separate chapter describing the experimental protocol and data analysis.

Conference papers on the topic "Surrogate experiments":

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Pacheco, Jorge E., Cristina H. Amon, and Susan Finger. "Incorporating Information From Replications Into Bayesian Surrogate Models." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dtm-48644.

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In some design domains, particularly rapidly evolving domains such as tissue engineering, analytical representations of the system do not exist. In these domains, the design process can be facilitated by the development of surrogate models that provide an understanding of the interactions of parameters and their influence on system performance, even though the models do not explain the underlying phenomena. Often, physical experiments are the only method for obtaining information about such systems. In particular, in bioengineering design domains, experiments are expensive and must be replicated to account for biological variability. Surrogate models can reduce the number of experiments needed and increase the value of the information gained through experimentation. In this paper, we present a framework for incorporating information from replications (repeated experiments) into Bayesian surrogate models. Within this framework, we develop uncertainty measurements for the prediction of the surrogate model. We illustrate the framework with two test cases using analytical functions. We then present a biomedical example used in the design of scaffold materials for the regeneration of bone tissue to show the use of Bayesian surrogates in exploratory design.
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Alley, M. D., S. F. Son, Mark Elert, Michael D. Furnish, William W. Anderson, William G. Proud, and William T. Butler. "BLAST LOADING EXPERIMENTS OF SURROGATE MODELS FOR TBI SCENARIOS." In SHOCK COMPRESSION OF CONDENSED MATTER 2009: Proceedings of the American Physical Society Topical Group on Shock Compression of Condensed Matter. AIP, 2009. http://dx.doi.org/10.1063/1.3295069.

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Kelly, Sean, and Corin Segal. "Experiments in Thermosensitive Cavitation of a Cryogenic Rocket Propellant Surrogate." In 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-1283.

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Amborn, Ericka K., Karim H. Muci-Küchler, and Brandon J. Hinz. "Experimental and Numerical Study of Soft Tissue Surrogate Behavior Under Ballistic Loading." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-85724.

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Studying the high strain rate behavior of soft tissues and soft tissue surrogates is of interest to improve the understanding of injury mechanisms during blast and impact events. Tests such as the split Hopkinson pressure bar have been successfully used to characterize material behavior at high strain rates under simple loading conditions. However, experiments involving more complex stress states are needed for the validation of constitutive models and numerical simulation techniques for fast transient events. In particular, for the case of ballistic injuries, controlled tests that can better reflect the effects induced by a penetrating projectile are of interest. This paper presents an experiment that tries to achieve that goal. The experimental setup involves a cylindrical test sample made of a translucent soft tissue surrogate that has a small pre-made cylindrical channel along its axis. A small caliber projectile is fired through the pre-made channel at representative speeds using an air rifle. High speed video is used in conjunction with specialized software to generate data for model validation. A Lagrangian Finite Element Method (FEM) model was prepared in ABAQUS/Explicit to simulate the experiments. Different hyperelastic constitutive models were explored to represent the behavior of the soft tissue surrogate and the required material properties were obtained from high strain rate test data reported in the open literature. The simulation results corresponding to each constitutive model considered were qualitatively compared against the experimental data for a single projectile speed. The constitutive model that provided the closest match was then used to perform an additional simulation at a different projectile velocity and quantitative comparisons between numerical and experimental results were made. The comparisons showed that the Marlow hyperelastic model available in ABAQUS/Explicit was able to produce a good representation of the soft tissue surrogate behavior observed experimentally at the two projectile speeds considered.
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Booker, Andrew, Paul Frank, J. Dennis, Jr., Douglas Moore, and David Serafini. "Managing surrogate objectives to optimize a helicopter rotor design - Further experiments." In 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1998. http://dx.doi.org/10.2514/6.1998-4717.

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Palar, Pramudita S., and Koji Shimoyama. "Kriging with Composite Kernel Learning for Surrogate Modeling in Computer Experiments." In AIAA Scitech 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-2209.

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Viana, Felipe A. C., Christian Gogu, and Raphael T. Haftka. "Making the Most Out of Surrogate Models: Tricks of the Trade." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28813.

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Design analysis and optimization based on high-fidelity computer experiments is commonly expensive. Surrogate modeling is often the tool of choice for reducing the computational burden. However, even after years of intensive research, surrogate modeling still involves a struggle to achieve maximum accuracy within limited resources. This work summarizes advanced and yet simple statistical tools that help. We focus on four techniques with increasing popularity in the design automation community: (i) screening and variable reduction in both the input and the output spaces, (ii) simultaneous use of multiple surrogates, (iii) sequential sampling and optimization, and (iv) conservative estimators.
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Grimm, Matthew V., Karim H. Muci-Küchler, Brandon J. Hinz, and Shawn M. Walsh. "Comparison of Numerical and Experimental Results of Small Scale Compressed Gas Blast Experiments Involving a Surrogate Head Form." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87663.

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Exposure to a shock wave from an explosive blast can result in injury to the human body even if external signs of trauma are not present. Gaining a better understanding of the mechanisms contributing to those injuries can result in the design of better personal protective equipment (PPE). Compressed gas blast experiments can be conveniently used to explore the mechanical response of PPE systems and instrumented surrogate head forms to blast loading scenarios in a laboratory environment. Likewise, numerical simulations can be used to study relevant field variables related to the compressed gas blast and its effects on the target. In this regard, experimental data is needed to validate simulation results. This paper presents an experiment that uses a small scale compressed gas blast generator to explore the pressure distribution around a surrogate head form due to blast loading. The compressed gas blast generator is an open-end shock tube which creates a shock wave when the diaphragm that separates the high pressure and low pressure (ambient air) regions ruptures. The overpressures on selected locations of the surrogate head form were measured with piezoelectric pressure sensors and the data was processed to obtain positive phase durations and positive phase impulses. The surrogate head form was positioned off-axis from the exit of the compressed gas blast generator to preclude the discharge flow from affecting the overpressure measurements. A three-dimensional Coupled Eulerian-Lagrangian (CEL) model of the experiment described above was prepared in Abaqus/Explicit. Selected numerical and experimental results were compared and there was good agreement between them.
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Doty, John. "Multi-Variate Designed Experiments for Development of a Wing Weight Surrogate Model." In 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-5.

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Bartram, Gregory W., Ricardo Perez, and Benjamin P. Smarslok. "Surrogate Modeling of Full-Field Pressure Measurements from Supersonic Wind Tunnel Experiments." In 18th AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-0430.

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Reports on the topic "Surrogate experiments":

1

Cooper, W. E. Implementation Plan: Jasper Management Prestart Review (Surrogate Material Experiments). Office of Scientific and Technical Information (OSTI), September 2000. http://dx.doi.org/10.2172/792744.

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Cooper, W. E. Plan of Action: JASPER Management Prestart Review (Surrogate Material Experiments). Office of Scientific and Technical Information (OSTI), September 2000. http://dx.doi.org/10.2172/791434.

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Elliott, J. Hydra modeling of experiments to study ICF capsule fill hole dynamics using surrogate targets. Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/925990.

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Escher, J., J. Burke, F. Dietrich, S. Lesher, N. Scielzo, I. Thompson, and W. Younes. Cross Sections for Neutron-induced Reactions on Actinide Targets Extracted from Surrogate Experiments: A Status Report. Office of Scientific and Technical Information (OSTI), October 2009. http://dx.doi.org/10.2172/971410.

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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The first dataset consisted of transmission loss (TL) fields for single realizations of turbulence. The second dataset consisted of average TL fields for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5–7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.
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Williams, Brian J. Tractable Experiment Design via Mathematical Surrogates. Office of Scientific and Technical Information (OSTI), February 2016. http://dx.doi.org/10.2172/1239923.

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Cooper, W. E. Plan of Action: JASPER Management Prestart Review (Surrogate Material Experiment). Office of Scientific and Technical Information (OSTI), December 2000. http://dx.doi.org/10.2172/15006162.

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Connolly, Michael. Aluminum Clad Spent Nuclear Fuel Task 6: Surrogate Sample Preparation and Validation Experiment Test Plan. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1469390.

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Moore, Derek, Joshua Goliber, Dustin Isereau, and Michael Gudaitis. Surrogate Joint Aerial Layer Network (JALN) Experiment: Applications of Commercial-Off-The-Shelf Technologies for Researching Future JALN Challenges. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada616812.

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Burke, J. T., R. O. Hughes, J. E. Escher, N. D. Scielzo, R. J. Casperson, J. J. Ressler, A. Saastamoinen, et al. Zirconium and Yttrium (p, d) Surrogate Nuclear Reactions: Measurement and determination of gamma-ray probabilities: Experimental Physics Report. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1400082.

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