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Journal articles on the topic "Physical Predictions":
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Nijland, Rinske H. M., Erwin E. H. van Wegen, Barbara C. Harmeling-van der Wel, and Gert Kwakkel. "Accuracy of Physical Therapists' Early Predictions of Upper-Limb Function in Hospital Stroke Units: The EPOS Study." Physical Therapy 93, no. 4 (April 1, 2013): 460–69. http://dx.doi.org/10.2522/ptj.20120112.
Background Early prediction of outcome after stroke is becoming increasingly important, as most patients are discharged from hospital stroke units within several days after stroke. Objectives The primary purposes of this study were: (1) to determine the accuracy of physical therapists' predictions at hospital stroke units regarding upper-limb (UL) function, (2) to develop a computational prediction model (CPM), and (3) to compare the accuracy of physical therapists' and the CPM's predictions. Secondary objectives were to explore the impact of timing on the accuracy of the physical therapists' and CPM's predictions and to investigate the direction of the difference between predicted and observed outcomes. Finally, this study investigated whether the accuracy of physical therapists' predictions was affected by their experience in stroke rehabilitation. Design A prospective cohort study was conducted. Methods Physical therapists made predictions at 2 time points—within 72 hours after stroke onset (T72h) and at discharge from the hospital stroke unit (Tdischarge)—about UL function after 6 months in 3 categories, derived from the Action Research Arm Test. At the same time, clinical variables were measured to derive a CPM. The accuracy of the physical therapists' and CPM's predictions was evaluated by calculating Spearman rank correlation coefficients (rs) between predicted and observed outcomes. Results One hundred thirty-one patients and 20 physical therapists participated in the study. For the T72h assessment, the rs value between predicted and observed outcomes was .63 for the physical therapists' predictions and .75 for the CPM's predictions. For the Tdischarge assessment, the rs value for the physical therapists' predictions improved to .75, and the rs value for the CPM's predictions improved slightly to .76. Limitations Physical therapists administered a test battery every 3 days, which may have enhanced the accuracy of prediction. Conclusions The accuracy of the physical therapists' predictions at T72h was lower than that of the CPM's predictions. At Tdischarge, the physical therapists' and CPM's predictions are about equally accurate.
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Zhao, Bo. "Studying on the Fiber Diameter of Polypropylene (PP) Spunbonding Fabric by Means of Artificial Neural Network Model and Physical Model." Key Engineering Materials 426-427 (January 2010): 356–60. http://dx.doi.org/10.4028/www.scientific.net/kem.426-427.356.
In this work, the artificial neural network model and physical model are established and utilized for predicting the fiber diameter of polypropylene(PP) spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the physical model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.
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Hafri, Alon, Michael Bonner, and Chaz Firestone. "Visual predictions from physical relations." Journal of Vision 20, no. 11 (October 20, 2020): 1615. http://dx.doi.org/10.1167/jov.20.11.1615.
Hou, Yuanyuan, Shiyu Wang, Bing Bai, H. C. Stephen Chan, and Shuguang Yuan. "Accurate Physical Property Predictions via Deep Learning." Molecules 27, no. 5 (March 3, 2022): 1668. http://dx.doi.org/10.3390/molecules27051668.
Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory accuracy even further. Here, we proposed a deep-learning architecture model, namely Bidirectional long short-term memory with Channel and Spatial Attention network (BCSA), of which the training process is fully data-driven and end to end. It is based on data augmentation and SMILES tokenization technology without relying on auxiliary knowledge, such as complex spatial structure. In addition, our model takes the advantages of the long- and short-term memory network (LSTM) in sequence processing. The embedded channel and spatial attention modules in turn specifically identify the prime factors in the SMILES sequence for predicting properties. The model was further improved by Bayesian optimization. In this work, we demonstrate that the trained BSCA model is capable of predicting aqueous solubility. Furthermore, our proposed method shows noticeable superiorities and competitiveness in predicting oil–water partition coefficient, when compared with state-of-the-art graphs models, including graph convoluted network (GCN), message-passing neural network (MPNN), and AttentiveFP.
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Bianco, Valentina, Alessandra Finisguerra, Sonia Betti, Giulia D’Argenio, and Cosimo Urgesi. "Autistic Traits Differently Account for Context-Based Predictions of Physical and Social Events." Brain Sciences 10, no. 7 (July 1, 2020): 418. http://dx.doi.org/10.3390/brainsci10070418.
Autism is associated with difficulties in making predictions based on contextual cues. Here, we investigated whether the distribution of autistic traits in the general population, as measured through the Autistic Quotient (AQ), is associated with alterations of context-based predictions of social and non-social stimuli. Seventy-eight healthy participants performed a social task, requiring the prediction of the unfolding of an action as interpersonal (e.g., to give) or individual (e.g., to eat), and a non-social task, requiring the prediction of the appearance of a moving shape as a short (e.g., square) or a long (e.g., rectangle) figure. Both tasks consisted of (i) a familiarization phase, in which the association between each stimulus type and a contextual cue was manipulated with different probabilities of co-occurrence, and (ii) a testing phase, in which visual information was impoverished by early occlusion of video display, thus forcing participants to rely on previously learned context-based associations. Findings showed that the prediction of both social and non-social stimuli was facilitated when embedded in high-probability contexts. However, only the contextual modulation of non-social predictions was reduced in individuals with lower ‘Attention switching’ abilities. The results provide evidence for an association between weaker context-based expectations of non-social events and higher autistic traits.
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Bock, Frederic E., Sören Keller, Norbert Huber, and Benjamin Klusemann. "Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions." Materials 14, no. 8 (April 10, 2021): 1883. http://dx.doi.org/10.3390/ma14081883.
Within the fields of materials mechanics, the consideration of physical laws in machine learning predictions besides the use of data can enable low prediction errors and robustness as opposed to predictions only based on data. On the one hand, exclusive utilization of fundamental physical relationships might show significant deviations in their predictions compared to reality, due to simplifications and assumptions. On the other hand, using only data and neglecting well-established physical laws can create the need for unreasonably large data sets that are required to exhibit low bias and are usually expensive to collect. However, fundamental but simplified physics in combination with a corrective model that compensates for possible deviations, e.g., to experimental data, can lead to physics-based predictions with low prediction errors, also despite scarce data. In this article, it is demonstrated that a hybrid model approach consisting of a physics-based model that is corrected via an artificial neural network represents an efficient prediction tool as opposed to a purely data-driven model. In particular, a semi-analytical model serves as an efficient low-fidelity model with noticeable prediction errors outside its calibration domain. An artificial neural network is used to correct the semi-analytical solution towards a desired reference solution provided by high-fidelity finite element simulations, while the efficiency of the semi-analytical model is maintained and the applicability range enhanced. We utilize residual stresses that are induced by laser shock peening as a use-case example. In addition, it is shown that non-unique relationships between model inputs and outputs lead to high prediction errors and the identification of salient input features via dimensionality analysis is highly beneficial to achieve low prediction errors. In a generalization task, predictions are also outside the process parameter space of the training region while remaining in the trained range of corrections. The corrective model predictions show substantially smaller errors than purely data-driven model predictions, which illustrates one of the benefits of the hybrid modelling approach. Ultimately, when the amount of samples in the data set is reduced, the generalization of the physics-related corrective model outperforms the purely data-driven model, which also demonstrates efficient applicability of the proposed hybrid modelling approach to problems where data is scarce.
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Bratholm, Lars A., Will Gerrard, Brandon Anderson, Shaojie Bai, Sunghwan Choi, Lam Dang, Pavel Hanchar, et al. "A community-powered search of machine learning strategy space to find NMR property prediction models." PLOS ONE 16, no. 7 (July 20, 2021): e0253612. http://dx.doi.org/10.1371/journal.pone.0253612.
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties.
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Chen, Zhewu, Zhanda Huang, Yong Guo, and Guibing Li. "Prediction of Mechanical Properties of Thin-Walled Bar with Open Cross-Section under Restrained Torsion." Coatings 12, no. 5 (April 21, 2022): 562. http://dx.doi.org/10.3390/coatings12050562.
Thin-walled bars with an open cross-section are widely used in mechanical structures where weight and size control are particularly required. Thus, this paper attempts to propose a theoretical model for predicting the mechanical properties of a thin-walled bar with an open cross-section under restrained torsion. Firstly, a theoretical model with predictions of shear stress, buckling normal stress, and secondary shear stress of the thin-walled bar with open cross-section under the condition of restrained torsion was developed based on torsion theory. Then, physical test and finite element modeling data were employed to validate the theoretical predictions. The results indicate that the theoretical predictions show good agreements with data of finite element modeling and experiments. Therefore, the proposed theoretical model could be used for the prediction of the mechanical response of a thin-walled bar with an open annular section under restrained torsion.
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Oh, Kyoungcheol, Eui-Jong Kim, and Chang-Young Park. "A Physical Model-Based Data-Driven Approach to Overcome Data Scarcity and Predict Building Energy Consumption." Sustainability 14, no. 15 (August 2, 2022): 9464. http://dx.doi.org/10.3390/su14159464.
Predicting building energy consumption needs to be anticipated to save building energy and effectively control the predictions. This study depicted the target building as a physical model to improve the learning performance in a data-scarce environment and proposed a model that uses simulation results as the input for a data-driven model. Case studies were conducted with different quantities of data. The proposed hybrid method proposed in this study showed a higher prediction accuracy showing a cvRMSE of 22.8% and an MAE of 6.1% than using the conventional data-driven method and satisfying the tolerance criteria of ASHRAE Guideline 14 in all the test cases.
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Feig, Michael, and Charles L. Brooks. "Evaluating CASP4 predictions with physical energy functions." Proteins: Structure, Function, and Genetics 49, no. 2 (September 3, 2002): 232–45. http://dx.doi.org/10.1002/prot.10217.
Dissertations / Theses on the topic "Physical Predictions":
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Grindley, Emma J. "Predicting adherence in injury rehabilitation utility of a screening tool and physical therapists' predictions /." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3931.
Lai, Wang Chun. "Characterisations of different El Nino types, their physical causes and predictions." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/271824.
El Niño Southern Oscillation (ENSO) is the most important interannual mode of climate variability in the tropical Pacific affecting the globe through teleconnections. The evolution of ENSO is studied with focus on individual El Nino (EN) events; factors and processes explaining the behaviours of different EN flavours are identified. The comparison to model simulations reveals a number of biases that explain differences in model behaviour. Based on reanalysis data, ENs are divided into Central Pacific (CPEN), Eastern Pacific (EPEN), and Hybrid (HBEN). ENs are found to form a continuous spectrum of events with CPEN and EPEN as its end members depending on: (1) the Western Pacific subsurface potential temperature anomaly (PTA) about 1 year before the EN peak, and (2) the Western to Central Pacific cumulative zonal wind anomaly (ZWA) between the onset and peak of the EN. Using these two parameters, about 70% of the total variance of the maximum EN SSTA can be explained up to 6 months in advance. ZWA describes the potential for triggering Kelvin waves for a given initial West Pacific recharge state as captured by PTA. A cross-validated statistical model is developed to hindcast the 1980-2016 Nov-Dec-Jan (NDJ) mean Niño3.4 SSTA based on the two parameters. The model is comparable to, or even outperforms, many NOAA Climate Prediction Centre's statistical models during the boreal spring predictability barrier. The explained variance between observed and predicted NDJ Niño3.4 SSTA at a lead-time of 8 months is 57% using five years for cross-validation. Predictive skills are lower after 2000 when the mean climate state is more La Niña-like due to stronger equatorial easterly ZWA caused by an intensification of both, Walker and Hadley cell. The ability of climate models to simulate and predict EN is assessed with data from the Climate Model Inter-comparison Project 5 (CMIP5). Most models are able to capture the main features of different EN types. But models struggle to reproduce large intensity ENs as found in observations. This issue can be traced back to a failure to realistically simulate the oceanic recharged state and the subsequent Kelvin waves for intense EN. Causes of EN involve Kelvin waves that are triggered by westerly wind bursts (WWB). From higher temporal resolution of reanalysis data, WWBs above a certain threshold are required to trigger a Kelvin wave. Kelvin waves are triggered in locations of positive Ocean Heat Content (OHC) anomalies. Intensity, longitudinal coverage and duration of a WWB, the strength of the OHC anomaly and gradient influence the amplitude of Kelvin waves as they propagate. Synoptic pattern analysis suggests that most WWBs are caused by cyclones with the combination of an active Madden-Julian Oscillation. The NorESM is able to reproduce many characteristics of observed WWBs, OHC anomalies and their relation to Kelvin waves. However, differences are noticeable for the distribution of synoptic patterns causing WWBs in the model. In future work, climate models can be used to disentangle causes and effects of EN for correlations identified here with the ultimate goal to advance our understanding of ENSO, its variability and future changes.
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Grönroos, Jesper, and Christoffer Beiming. "Exotic Hadrons : Classification of Mass Models and Predictions for Non-Strange Dibaryons." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297543.
In this report we study theoretical models for calculating mass spectra of exotic hadrons and carry out numerical predictions for selected states. A brief introduction to the Standard Model and other key concepts are presented in order to contextualize and aid the reader. Four mass models are described and classified, applicable to different multiquark systems. Then, predictions for mass spectra of six non-strange dibaryon candidates are performed, using a simple mass formula with parameters fixed from experimentally determined baryon masses. Finally, results are discussed in relation to other existing work on the subject.
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Gunnesby, Michael. "On Flow Predictions in Fuel Filler Pipe Design - Physical Testing vs Computational Fluid Dynamics." Thesis, Linköpings universitet, Mekanisk värmeteori och strömningslära, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-117534.
The development of a fuel filler pipe is based solely on experience and physical experiment. The challenge lies in designing the pipe to fulfill the customer needs. In other words designing the pipe such as the fuel flow does not splash back on the fuel dispenser causing a premature shut off. To improve this “trial-and-error” based development a computational fluid dynamics (CFD) model of the refueling process is investigated. In this thesis a CFD model has been developed that can predict the fuel flow in the filler pipe. Worst case scenario of the refueling process is during the first second when the tank is partially filled. The most critical fluid is diesel due to the commercially high volume flow of 55 l/min. Due to limitations of computational resources the simulations are focused on the first second of the refueling process. The challenge in this project is creating a CFD model that is time efficient, thus require the least amount of computational resources necessary to provide useful information. A multiphase model is required to simulate the refueling process. In this project the implicit volume of fluid (VOF) has been used which has previously proven to be a suitable choice for refueling simulations. The project is divided into two parts. Part one starts with experiments and simulations of a simplified fuel system with water as acting liquid with a Reynolds number of 90 000. A short comparison between three different turbulence models has been investigated (LES, DES and URANS) where the most promising turbulence model is URANS, specifically the SST k-ω model. A sensitivity analysis was performed on the chosen turbulence model. Between the chosen mesh and the densest mesh the difference of streamwise velocity in the boundary layer was 2.6 %. The chosen mesh with 1.9 M cells and a time step of 1e-4 s was found to be the best correlating model with respect to the experiments. In part two a real fuel filling system was investigated both with experiments and simulations with the same computational model as the chosen one from part one. The change of fluid and geometry resulted in a lower Reynolds number of 12 000. Two different versions of the fuel system was investigated; with a bypass pipe and without a bypass pipe. Because of a larger volumetric region the resulting mesh had 3.7 M cells. The finished model takes about 230 h on a local workstation with 11 cores. On a cluster with 200 cores the same simulation takes 30 h. The resulting model suffered from interpolation errors at the inlet which resulted in a volume flow of 50 l/min as opposed to 55 l/min in the experiments. Despite the difference the model could capture the key flow characteristics. With the developed model a new filler pipe can be easily implemented and provide results in shorter time than a prototype filler pipe can be ordered. This will increase the chances of ordering one single prototype that fulfills all requirements. While the simulation model cannot completely replace verification by experiments it provides information that transforms the development of the filler pipe to knowledge based development.
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Erunal, Ebru. "Sturcuture And Activity Predictions On Mono- And Bi-metallic Catalysts." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607178/index.pdf.
The purpose of this study is to simulate Pt&ndash IB (IB=Ag, Au, Cu) and PtPd bimetallic catalysts with Monte Carlo method for 201, 586, 1289, and 2406 atom containing clusters in the temperature range between 298&ndash 1000K. The simulations were based on a coordination-dependent potential model in which
binary interaction parameters were used. The binary interaction parameters were determined from the available thermodynamic data and classical thermodynamics mixing rules. The equilibrium structure of the clusters was dictated as a perfect cubo-octohedral shape. In the first part of this study, Pt&ndash Ib bimetallics were modelled in order to test the Monte Carlo program against the previously published work. In the second part of the study, the surface composition of PtPd bimetallic catalysts as a function of temperature and cluster size were estimated in order to offer further insight to the catalytic activity for CO oxidation reaction. It was found that at low temperatures Pd segregation took place on the catalyst. The Monte Carlo predictions were in good agreement with the published experimental data on the surface compositions.
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Chu, Yi-Fei. "The incorporation of hourly goes data in a surface heat flux model and its impacts on operational temperature predictions in bodies of water /." The Ohio State University, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=osu14879491500689.
Through distributed stream network routing, it has quantitatively been shown that the relationship between flow travel time and discharge varies strongly nonlinearly with stream stage and with catchment-specific properties. Physically derived distributions of water travel times through a stream network were successfully used to parameterise the streamflow response function of a compartmental hydrological model. Predictions were found to improve compared to conventional statistically based parameterisation schemes, for most of the modelled scenarios, particularly for peakflow conditions. A Fourier spectral analysis of 55-110 years of daily discharge time series from 79 unregulated catchments in Sweden revealed that the discharge power spectral slope has gradually increased over time, with significant increases for 58 catchments. The results indicated that the catchment scaling function power spectrum had steepened in most of the catchments for which historical precipitation series were available. These results suggest that (local) land-use changes within the catchments may affect the discharge power spectra more significantly than changes in precipitation (climate change). A case study from an agriculturally intense catchment using historical (from the 1880s) and modern stream network maps revealed that the average stream network flow distance as well as average water levels were substantially diminished over the past century, while average bottom slopes increased. The study verifies the hypothesis that anthropogenic changes (determined through scenario modelling using a 1D distributed routing model) of stream network properties can have a substantial influence on the travel times through the stream networks and thus on the discharge hydrographs. The findings stress the need for a more hydrodynamically based approach to adequately describe the variation of streamflow response, especially for predictions of higher discharges. An increased physical basis of response functions can be beneficial in improving discharge predictions during conditions in which conventional parameterisation based on historical flow patterns may not be possible - for example, for extreme peak flows and during periods of nonstationary conditions, such as during periods of climate and/or land use change.
QC 20150903
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Juri, Juan Ernesto. "Prediction of petro-physical properties for carbonates." Thesis, Heriot-Watt University, 2016. http://hdl.handle.net/10399/3120.
This thesis is concerned with the inversion of lattice pore-network model parameters of carbonate rocks using only the capillary pressure, and then the use of the inverted parameters to predict the water-flooding relative permeabilities of the carbonate rocks. Background: There has been a tendency to claim that pore-network modelling using three-dimensional micro-computed tomography or 3D mathematically created images can predict imbibition relative permeabilities for wettabilities other than strongly water/oil-wetting. This is based on the flexibility for matching data, which is a weakness of pore-network modelling. The method proposed in this thesis is important because a large percentage of the porosity in carbonates is microporosity. Conclusions: We applied stochastic inversion of lattice pore-network model parameters using Hamiltonian Dynamics (Hamiltonian Monte Carlo) to three carbonate rock samples and we predicted water-flooding relative permeabilities with good accuracy by using as constraint only routinely obtained data, such as mercury intrusion capillary pressure (MICP) and oil/water capillary pressure. We found that there is a strong correlation between the amount of microporosity and the volume exponent parameter. This suggests that when microporosity is ignored, the volume exponent will tend to be systematically strongly underestimated. HMC found large variability in wettability that causes mid-sized pores to be invaded at the same level of pressure as larger pores. The coexistence of these events reduces the tendency for preferential flow through large pores, resulting in more uniform flow at the pore scale compared with the case in which flow is restricted only to large pores. Mid-sized pores have an important effect on the connectivity because they could have higher contact angles than larger pores. Therefore, they do not spontaneously imbibe and shield larger pores, improving water-flooding displacement. The wettability of micropores could better explain the gentle curvature of the imbibition water relative permeability compared with the generally assumed mixed-wet large wettability model. The importance of the maximum and minimum observed capillary pressure is directly connected to accounting for the full pore-size distribution. Thus, the common assumption that microporosity can be ignored is unsatisfactory. The ranges of advancing contact angles obtained from the HMC inversion were wider than the ranges of apparent advancing contact angles obtained with analytical determinations in previous studies, and in one case our results were contradictory to the analytical determination. It follows that variability in advancing and receding contact angles is not reflected in the apparent contact angle data outside porous media. Apparent contact angle data outside porous media cannot completely characterise the wettability in porenetwork models because the data does not capture the contact angle variability in porous media. The existence of wetting films depends on the maximum capillary pressure during drainage, and thus wettability alteration during ageing. Our results suggest that matching both connate water at the maximum drainage capillary pressure before ageing and matching residual oil at the minimum imbibition capillary pressure leads to better estimation of the advancing and receding variability in the contact angles.
Roberts, Amy B. "Physical activity prediction using transtheoretical model and personality /." Available to subscribers only, 2005. http://proquest.umi.com/pqdweb?did=1095437441&sid=8&Fmt=2&clientId=1509&RQT=309&VName=PQD.
Jochum, Clemens, Martin G. Hicks, and Josef Sunkel, eds. Physical Property Prediction in Organic Chemistry. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74140-1.
1939-, Wyss Max, Shimazaki K. 1946-, and Ito Akihiko, eds. Seismicity patterns, their statistical significance and physical meaning. Basel: Birkhäuser Verlag, 1999.
Book chapters on the topic "Physical Predictions":
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Bauer, Fabian, Jessica Hagner, Peter Bretschneider, and Stefan Klaiber. "Improvement of the prediction quality of electrical load profiles with artificial neural networks." In Machine Learning for Cyber Physical Systems, 13–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_2.
AbstractAgainst the backdrop of the economically and ecologically optimal management of electrical energy systems, accurate predictions of consumption load profiles play an important role. On this basis, it is possible to plan and implement the use of controllable energy generation and storage systems as well as energy procurement with the required lead-time, taking into account the technical and contractual boundary conditions.The recorded electrical load profiles will increase considerably in the course of the digitization of the energy industry. In order to make the most accurate predictions possible, it is necessary to develop and investigate models that take account of the growing quantity structure and, due to the significantly higher number of observations, improve the forecasting quality as far as possible.Artificial neural networks (ANN) are increasingly being used to solve non-linear problems for a growing amount of data that is affected by human and other unpredictable influences. Consequently, the model approach of an ANN is chosen for predicting load profiles. Aim of the thesis is the simulative investigation and the evaluation of the quality and optimality of a prediction model based on an ANN for electrical load profiles.
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Ciancarini, Paolo, Francesco Poggi, Davide Rossi, and Alberto Sillitti. "Improving Bug Predictions in Multicore Cyber-Physical Systems." In Proceedings of 4th International Conference in Software Engineering for Defence Applications, 287–95. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27896-4_24.
Bortolussi, Luca, Francesca Cairoli, Nicola Paoletti, and Scott D. Stoller. "Conformal Predictions for Hybrid System State Classification." In From Reactive Systems to Cyber-Physical Systems, 225–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31514-6_13.
Clark, Timothy. "Molecular Orbital and Force-Field Calculations for Structure and Energy Predictions." In Physical Property Prediction in Organic Chemistry, 95–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74140-1_9.
Peltzer, Edward T., and Peter G. Brewer. "Practical Physical Chemistry and Empirical Predictions of Methane Hydrate Stability." In Coastal Systems and Continental Margins, 17–28. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-011-4387-5_3.
Lermusiaux, P. F. J., C. Evangelinos, R. Tian, P. J. Haley, J. J. McCarthy, N. M. Patrikalakis, A. R. Robinson, and H. Schmidt. "Adaptive Coupled Physical and Biogeochemical Ocean Predictions: A Conceptual Basis." In Computational Science - ICCS 2004, 685–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24688-6_89.
Lermusiaux, P. F. J., and C. S. Chiu. "Four-Dimensional Data Assimilation for Coupled Physical-Acoustical Fields." In Impact of Littoral Environmental Variability of Acoustic Predictions and Sonar Performance, 417–24. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0626-2_52.
Shaw, Robert J., Mark G. Potapczuk, and Colin S. Bidwell. "Predictions of Airfoil Aerodynamic Performance Degradation Due to Icing." In Numerical and Physical Aspects of Aerodynamic Flows IV, 19–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02643-4_2.
Garrett, Steven L. "Comfort for the Computationally Crippled." In Understanding Acoustics, 1–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44787-8_1.
Abstract The difference between engineering and science, and all other human activity, is the fact that engineers and scientists make quantitative predictions about measurable outcomes and can specify their uncertainty in such predictions. Because those predictions are quantitative, they must employ mathematics. This chapter is intended as review of some of the more useful mathematical concepts, strategies, and techniques that are employed in the description of vibrational and acoustical systems and in the calculation of their behavior. Topics in this review include techniques such as Taylor series expansions, integration by parts, and logarithmic differentiation. Equilibrium and stability considerations lead to relations between potential energies and forces. The concept of linearity leads to superposition and Fourier analysis. Complex numbers and phasors are introduced along with the techniques for their algebraic manipulation. The discussion of physical units is extended to include their use for predicting functional dependencies of resonance frequencies, quality factors, propagation speeds, flow noise, and other system behaviors using similitude and the Buckingham Π-theorem to form dimensionless variables. Linearized least-squares fitting is introduced as a method for extraction of experimental parameters and their uncertainties and error propagation is presented to allow those uncertainties to be combined.
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Utkin, Lev, Artem Petrov, and Andrei Konstantinov. "Modifications of SHAP for Local Explanation of Function-Valued Predictions Using the Divergence Measures." In Cyber-Physical Systems and Control II, 52–64. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20875-1_6.
Conference papers on the topic "Physical Predictions":
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Herzog, Franz. "Theory Predictions for LHC Higgs Boson Production." In The European Physical Society Conference on High Energy Physics. Trieste, Italy: Sissa Medialab, 2014. http://dx.doi.org/10.22323/1.180.0282.
Vogt, Andreas, Sven Moch, Jos Vermaseren, and Gary Soar. "Higher-order predictions from physical evolution kernels." In RADCOR 2009 - 9th International Symposium on Radiative Corrections (Applications of Quantum Field Theory to Phenomenology). Trieste, Italy: Sissa Medialab, 2010. http://dx.doi.org/10.22323/1.092.0053.
Gorbunov, Dmitry. "nuMSM: the model, its predictions and experimental tests." In The European Physical Society Conference on High Energy Physics. Trieste, Italy: Sissa Medialab, 2016. http://dx.doi.org/10.22323/1.234.0092.
Klasen, Michael, Benjamin Fuks, and Marthijn Sunder. "Precision predictions for associated gluino-gaugino production at the LHC." In The European Physical Society Conference on High Energy Physics. Trieste, Italy: Sissa Medialab, 2017. http://dx.doi.org/10.22323/1.314.0298.
Borschensky, Christoph, Benjamin Fuks, Anna Kulesza, and Daniel Schwartländer. "Precision predictions for scalar leptoquark pair production at the LHC." In The European Physical Society Conference on High Energy Physics. Trieste, Italy: Sissa Medialab, 2022. http://dx.doi.org/10.22323/1.398.0637.
Sutcliffe, William, Florian Bernlochner, Zoltan Ligeti, and Dean Robinson. "Precise predictions for $\Lambda_{b} \rightarrow \Lambda_{c} \ell^{-} \bar{\nu}_{\ell}$ decays." In European Physical Society Conference on High Energy Physics. Trieste, Italy: Sissa Medialab, 2020. http://dx.doi.org/10.22323/1.364.0265.
Papanastasiou, Andrew. "Fully-differential predictions for top pair-production and decay at high precision." In The European Physical Society Conference on High Energy Physics. Trieste, Italy: Sissa Medialab, 2017. http://dx.doi.org/10.22323/1.314.0456.
Rzehak, Heidi, Henning Bahl, and Nick Murphy. "Precise predictions of the mass of the discovered Higgs boson in supersymmetric scenarios." In The European Physical Society Conference on High Energy Physics. Trieste, Italy: Sissa Medialab, 2022. http://dx.doi.org/10.22323/1.398.0590.
Drechsel, Peter, Leonardo Galeta, Sven Heinemeyer, and Georg Ralf Weiglein. "Precise predictions for Higgs-masses in the Next-to-Minimal Supersymmetric Standard Model (NMSSM)." In The European Physical Society Conference on High Energy Physics. Trieste, Italy: Sissa Medialab, 2016. http://dx.doi.org/10.22323/1.234.0186.
Rebhan, Anton, and Frederic Brünner. "Predictions for production and decay of the pseudoscalar glueball from the Witten-Sakai-Sugimoto model." In The European Physical Society Conference on High Energy Physics. Trieste, Italy: Sissa Medialab, 2017. http://dx.doi.org/10.22323/1.314.0545.
Cheung, Kevin K. Tropical Cyclone Formation: Physical Processes and Predictions. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada627718.
Vecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond, and Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45300.
To make a prediction for seismic signal propagation, one needs to specify physical properties and subsurface ground structure of the site. This information is frequently unknown or estimated with significant uncertainty. This paper describes a methodology for probabilistic seismic ensemble prediction for vertically stratified soils and short ranges with no in situ site characterization. Instead of specifying viscoelastic site properties, the methodology operates with probability distribution functions of these properties taking into account analytical and empirical relationships among viscoelastic variables. This yields ensemble realizations of signal arrivals at specified locations where statistical properties of the signals can be estimated. Such ensemble predictions can be useful for preliminary site characterization, for military applications, and risk analysis for remote or inaccessible locations for which no data can be acquired. Comparison with experiments revealed that measured signals are not always within the predicted ranges of variability. Variance-based global sensitivity analysis has shown that the most significant parameters for signal amplitude predictions in the developed stochastic model are the uncertainty in the shear quality factor and the Poisson ratio above the water table depth.
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Brodsky, Stanley J. Scheme-Independent Predictions in QCD: Commensurate Scale Relations and Physical Renormalization Schemes. Office of Scientific and Technical Information (OSTI), December 1998. http://dx.doi.org/10.2172/9977.
Ford, S. R. Free-field Ground Motion Induced by Underground Explosions at Aqueduct Mesa with Predictions for Physical Experiment One (PE1). Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1605054.
Tenney, Craig M., Kevin Nicholas Long, and Jamie Michael Kropka. Predictions of Yield Strength Evolution Due to Physical Aging of 828 DGEBA/DEA using the Simplified Potential Energy Clock Model. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1498246.
Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.
Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.
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Verronen, P. T:, ed. 11 th International Workshop on Long-Term Changes and Trends in the Atmosphere, Book of Abstracts. Finnish Meteorological Institute, May 2022. http://dx.doi.org/10.35614/isbn.9789523361577.
The 11 th International Workshop on Long-Term Changes and Trends in the Atmosphere will be held between 30 May and 3 June, 2022, at the Finnish Meteorological Institute in Helsinki, Finland. The workshop is organised by the Finnish Meteorological Institute. The workshop gathers together more than 50 scientists from the EU, USA, India, Canada, Argentina, Norway, China, Switzerland, and UK. This report is the official abstract book of the workshop. The scientific topics include: ● Stratospheric and mesospheric observations ● Simulations and predictions of the stratosphere and mesosphere ● Changes in the ionosphere and thermosphere ● Dynamic, physical, chemical and radiative mechanisms ● Role of the stratosphere and mesosphere for climate The workshop is sponsored by the International Association of Geomagnetism and Aeronomy (IAGA) and the International Association of Meteorology and Atmospheric Sciences (IAMAS).
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Letcher, Theodore, Sandra LeGrand, and Christopher Polashenski. The Blowing Snow Hazard Assessment and Risk Prediction model : a Python based downscaling and risk prediction for snow surface erodibility and probability of blowing snow. Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43582.
Blowing snow is an extreme terrain hazard causing intermittent severe reductions in ground visibility and snow drifting. These hazards pose significant risk to operations in snow-covered regions. While many ingredients-based forecasting methods can be employed to predict where blowing snow is likely to occur, there are currently no physically based tools to predict blowing snow from a weather forecast. However, there are several different process models that simulate the transport of snow over short distances that can be adapted into a terrain forecasting tool. This report documents a downscaling and blowing-snow prediction tool that leverages existing frameworks for snow erodibility, lateral snow transport, and visibility, and applies these frameworks for terrain prediction. This tool is designed to work with standard numerical weather model output and user-specified geographic models to generate spatially variable forecasts of snow erodibility, blowing snow probability, and deterministic blowing-snow visibility near the ground. Critically, this tool aims to account for the history of the snow surface as it relates to erodibility, which further refines the blowing-snow risk output. Qualitative evaluations of this tool suggest that it can provide more precise forecasts of blowing snow. Critically, this tool can aid in mission planning by downscaling high-resolution gridded weather forecast data using even higher resolution terrain dataset, to make physically based predictions of blowing snow.
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Ridout, James. Physics Parameterization for Seasonal Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574085.
Ridout, James. Physics Parameterization for Seasonal Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada598266.
