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Journal articles on the topic 'Stochastic matching models'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Dewilde, Patrick. "Stochastic models based on moment matching." Communications in Information and Systems 20, no. 2 (2020): 209–48. http://dx.doi.org/10.4310/cis.2020.v20.n2.a5.

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Hu, Lin Y., and Sandra Jenni. "History Matching of Object-Based Stochastic Reservoir Models." SPE Journal 10, no. 03 (2005): 312–23. http://dx.doi.org/10.2118/81503-pa.

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3

Mairesse, Jean, and Pascal Moyal. "Editorial introduction to the special issue on stochastic matching models, matching queues and applications." Queueing Systems 96, no. 3-4 (2020): 357–58. http://dx.doi.org/10.1007/s11134-021-09690-2.

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Comte, Céline. "Stochastic non-bipartite matching models and order-independent loss queues." Stochastic Models 38, no. 1 (2021): 1–36. http://dx.doi.org/10.1080/15326349.2021.1962352.

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Meszáros, András, János Papp, and Miklós Telek. "Fitting traffic traces with discrete canonical phase type distributions and Markov arrival processes." International Journal of Applied Mathematics and Computer Science 24, no. 3 (2014): 453–70. http://dx.doi.org/10.2478/amcs-2014-0034.

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Abstract Recent developments of matrix analytic methods make phase type distributions (PHs) and Markov Arrival Processes (MAPs) promising stochastic model candidates for capturing traffic trace behaviour and for efficient usage in queueing analysis. After introducing basics of these sets of stochastic models, the paper discusses the following subjects in detail: (i) PHs and MAPs have different representations. For efficient use of these models, sparse (defined by a minimal number of parameters) and unique representations of discrete time PHs and MAPs are needed, which are commonly referred to

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Pankov, Vikentii, and Oleg Granichin. "SPSA Algorithm for Matching of Historical Data for Complex Non-Gaussian Geological Models." Cybernetics and Physics, Volume 11, 2022, Number 1 (June 2, 2022): 18–24. http://dx.doi.org/10.35470/2226-4116-2022-11-1-18-24.

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History matching is the process of integrating dynamic production data in the reservoir model. It consists in estimation of uncertain model parameters such that oil or water production data from flow simulation become close to observed dynamic data. Various optimization methods can be used to estimate the model parameters. Simultaneous perturbation stochastic approximation (SPSA) is one of the stochastic approximation algorithms. It requires only two objective function measurements for gradient approximation per iteration. Also parameters estimated by this algorithm might converge to their tru

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Iwata, Tomoharu, Tsutomu Hirao, and Naonori Ueda. "Unsupervised Cluster Matching via Probabilistic Latent Variable Models." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 445–51. http://dx.doi.org/10.1609/aaai.v27i1.8558.

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We propose a probabilistic latent variable model for unsupervised cluster matching, which is the task of finding correspondences between clusters of objects in different domains. Existing object matching methods find one-to-one matching. The proposed model finds many-to-many matching, and can handle multiple domains with different numbers of objects. The proposed model assumes that there are an infinite number of latent vectors that are shared by all domains, and that each object is generated using one of the latent vectors and a domain-specific linear projection. By inferring a latent vector

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Zhou, Deng Rong, Jian Chun Gong, and Fang Ling Fan. "A Stochastic Resonance Phenomenon in Linear Models." Applied Mechanics and Materials 401-403 (September 2013): 1301–4. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1301.

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When there exist certain kinds of matching in an electric system between the nonlinear input and noise, amplifying the input noise may dramatically increase the output SNR other than decrease it. And stochastic resonance is a phenomenon that when noise is input at certain amplitude the output SNR reaches its peak. Generalized stochastic resonance is the kind of nonlinear phenomena that the output (output SNR, output mean value, etc.) is a non-monotonic function of some parameter of noise (amplitude, correlation time) or input (amplitude, frequency). We studied the phenomenon of stochastic reso

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Tsioulou, Alexandra, Alexandros A. Taflanidis, and Carmine Galasso. "Modification of stochastic ground motion models for matching target intensity measures." Earthquake Engineering & Structural Dynamics 47, no. 1 (2017): 3–24. http://dx.doi.org/10.1002/eqe.2933.

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Brignone, Riccardo, Ioannis Kyriakou, and Gianluca Fusai. "Moment-matching approximations for stochastic sums in non-Gaussian Ornstein–Uhlenbeck models." Insurance: Mathematics and Economics 96 (January 2021): 232–47. http://dx.doi.org/10.1016/j.insmatheco.2020.12.002.

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Alqurashi, M., and J. Wang. "Impacts of stochastic models on real-time 3D UAV mapping." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 35–44. http://dx.doi.org/10.5194/isprsarchives-xl-1-35-2014.

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In UAV mapping using direct geo-referencing, the formation of stochastic model generally takes into the account the different types of measurements required to estimate the 3D coordinates of the feature points. Such measurements include image tie point coordinate measurements, camera position measurements and camera orientation measurements. In the commonly used stochastic model, it is commonly assumed that all tie point measurements have the same variance. In fact, these assumptions are not always realistic and thus, can lead to biased 3D feature coordinates. Tie point measurements for differ

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Tadjer, Amine, Reider B. Bratvold, and Remus G. Hanea. "Efficient Dimensionality Reduction Methods in Reservoir History Matching." Energies 14, no. 11 (2021): 3137. http://dx.doi.org/10.3390/en14113137.

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Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncer

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Backe, Stian, Mohammadreza Ahang, and Asgeir Tomasgard. "Stable stochastic capacity expansion with variable renewables: Comparing moment matching and stratified scenario generation sampling." Applied Energy 302 (August 18, 2021): 117538. https://doi.org/10.1016/j.apenergy.2021.117538.

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This paper examines the importance of including operational scenarios representing short-term stochasticity in the long-term capacity expansion models with high shares of variable renewables. As scenario generation routines often are probabilistic, for example based on sampling, it is crucial that they ensure stable results in the capacity expansion model, so that it is the underlying uncertainty that decides the optimal solution, and not the approximation of that uncertainty in the model. However, it is unclear which operational scenario properties that are important to ensure good results an

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Wu, Mingqi, Yinsen Miao, Neilkunal Panchal, et al. "Stochastic clustering and pattern matching for real-time geosteering." GEOPHYSICS 84, no. 5 (2019): ID13—ID24. http://dx.doi.org/10.1190/geo2018-0781.1.

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We have developed a Bayesian statistical framework for quantitative geosteering in real time. Two types of contemporary geosteering approaches, model based and stratification based, are introduced. The latter is formulated as a Bayesian optimization procedure: The log from a pilot reference well is used as a stratigraphic signature of the geologic structure in a given region; the observed log sequence acquired along the wellbore is projected into the stratigraphic domain given a proposed earth model and directional survey; the pattern similarity between the converted log and the signature is m

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Yan, Yutong, Wei Zhang, Yahua Yin, and Weidong Huo. "An Ornstein–Uhlenbeck Model with the Stochastic Volatility Process and Tempered Stable Process for VIX Option Pricing." Mathematical Problems in Engineering 2022 (October 6, 2022): 1–14. http://dx.doi.org/10.1155/2022/4018292.

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To effectively fit the dynamics and structure of frequent small jumps and sparse large jumps in the VIX time series, we introduce the tempered stable process (the CTS process and CGMY process) into the Ornstein–Uhlenbeck (OU) stochastic volatility model to build an OU model with the stochastic volatility process and tempered stable process. Based on two different assumptions for the underlying assets, we derive the formula of pricing models via two methods. Empirical studies are conducted to prove that our pricing models have a better performance in matching the VIX options. Furthermore, we fi

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Cadas, Arnaud, Josu Doncel, Jean-Michel Fourneau, and Ana Busic. "Flexibility can Hurt Dynamic Matching System Performance." ACM SIGMETRICS Performance Evaluation Review 49, no. 3 (2022): 37–42. http://dx.doi.org/10.1145/3529113.3529126.

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We study the performance of stochastic matching models with general compatibility graphs. Items of different classes arrive to the system according to independent Poisson processes. Upon arrival, an item is matched with a compatible item according to the First Come First Matched discipline and both items leave the system immediately. If there are no compatible items, the new arrival joins the queue of unmatched items of the same class. Compatibilities between item classes are defined by a connected graph, where nodes represent the classes of items and the edges the compatibilities between item

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Kwon, Jay Hyoun, and Christopher Jekeli. "The effect of stochastic gravity models in airborne vector gravimetry." GEOPHYSICS 67, no. 3 (2002): 770–76. http://dx.doi.org/10.1190/1.1484520.

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Measurements of specific force using inertial measurement units (IMU) combined with Global Positioning System (GPS) accelerometry can be used on an airborne platform to determine the total gravitational vector. Traditional methods, originating with inertial surveying systems and based on Kalman filtering, rely on choosing an appropriate stochastic model for the gravity disturbance components included in the set of system error states. An alternative procedure that uses no a priori stochastic model has proven to be as effective, or moreso, in extracting the gravity vector from airborne IMU/GPS

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Tamegawa, Kenichi, and Shin Fukuda. "EXPECTATION ERRORS IN CREDIT MARKET AND BUSINESS CYCLES." Macroeconomic Dynamics 20, no. 5 (2016): 1359–80. http://dx.doi.org/10.1017/s1365100514000923.

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This study demonstrates how expectation errors in a credit market generate economic fluctuations. To this end, we employ simulation analysis using a dynamic stochastic general equilibrium model. Our model includes two building blocks that are not included in the standard models: the banking sector and matching friction in the labor market. By introducing the banking sector, we can confirm that if economic agents fallaciously expect a rise in future asset prices, such expectations will cause an economic boom and bust. The variation of this fluctuation is quite large and the recession short-live

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Nguatem, W., M. Drauschke, and H. Mayer. "AUTOMATIC GENERATION OF BUILDING MODELS WITH LEVELS OF DETAIL 1-3." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 649–54. http://dx.doi.org/10.5194/isprs-archives-xli-b3-649-2016.

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We present a workflow for the automatic generation of building models with levels of detail (LOD) 1 to 3 according to the CityGML standard (Gröger et al., 2012). We start with orienting unsorted image sets employing (Mayer et al., 2012), we compute depth maps using semi-global matching (SGM) (Hirschmüller, 2008), and fuse these depth maps to reconstruct dense 3D point clouds (Kuhn et al., 2014). Based on planes segmented from these point clouds, we have developed a stochastic method for roof model selection (Nguatem et al., 2013) and window model selection (Nguatem et al., 2014). We demonstrat

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Nguatem, W., M. Drauschke, and H. Mayer. "AUTOMATIC GENERATION OF BUILDING MODELS WITH LEVELS OF DETAIL 1-3." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 649–54. http://dx.doi.org/10.5194/isprsarchives-xli-b3-649-2016.

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We present a workflow for the automatic generation of building models with levels of detail (LOD) 1 to 3 according to the CityGML standard (Gröger et al., 2012). We start with orienting unsorted image sets employing (Mayer et al., 2012), we compute depth maps using semi-global matching (SGM) (Hirschmüller, 2008), and fuse these depth maps to reconstruct dense 3D point clouds (Kuhn et al., 2014). Based on planes segmented from these point clouds, we have developed a stochastic method for roof model selection (Nguatem et al., 2013) and window model selection (Nguatem et al., 2014). We demonstrat

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21

Claes, Steven, and Hans Janssen. "Towards stochastic generation of 3D pore network models of building materials." MATEC Web of Conferences 282 (2019): 02022. http://dx.doi.org/10.1051/matecconf/201928202022.

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Pore-scale-based prediction of the hygric properties of porous building materials is on the rise as an attractive alternative for the current experimental procedure. Pore-scale simulations do however require a complete pore network model for the building material. With the currently available characterization techniques, such complete pore network model cannot be established, instead typically fragmented direct (pores sizes, shapes, positions, connections, …) or indirect (pore size distribution, pore surface area, …) information is obtained. The aim of this paper is to present stochastic pore

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22

Benigno, Gianluca, Andrew Foerster, Christopher Otrok, and Alessandro Rebucci. "Estimating macroeconomic models of financial crises: An endogenous regime‐switching approach." Quantitative Economics 16, no. 1 (2025): 1–47. https://doi.org/10.3982/qe2038.

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We develop a new model of cycles and crises in emerging markets, featuring an occasionally binding borrowing constraint and stochastic volatility, and estimate it with quarterly data for Mexico since 1981. We propose an endogenous regime‐switching formulation of the occasionally binding borrowing constraint, develop a general perturbation method to solve the model, and estimate it using Bayesian methods. We find that the model fits the Mexican data well without systematically relying on large shocks, matching the typical stylized facts of emerging market business cycles and Mexico's history of

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23

Shutts, Glenn, and Alfons Callado Pallarès. "Assessing parametrization uncertainty associated with horizontal resolution in numerical weather prediction models." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2018 (2014): 20130284. http://dx.doi.org/10.1098/rsta.2013.0284.

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The need to represent uncertainty resulting from model error in ensemble weather prediction systems has spawned a variety of ad hoc stochastic algorithms based on plausible assumptions about sub-grid-scale variability. Currently, few studies have been carried out to prove the veracity of such schemes and it seems likely that some implementations of stochastic parametrization are misrepresentations of the true source of model uncertainty. This paper describes an attempt to quantify the uncertainty in physical parametrization tendencies in the European Centre for Medium-Range Weather Forecasts (

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24

BOVIER, ANTON, JIŘÍ ČERNÝ, and OSTAP HRYNIV. "THE OPINION GAME: STOCK PRICE EVOLUTION FROM MICROSCOPIC MARKET MODELING." International Journal of Theoretical and Applied Finance 09, no. 01 (2006): 91–111. http://dx.doi.org/10.1142/s0219024906003421.

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We propose a class of Markovian agent based models for the time evolution of a share price in an interactive market. The models rely on a microscopic description of a market of buyers and sellers who change their opinion about the stock value in a stochastic way. The actual price is determined in realistic way by matching (clearing) offers until no further transactions can be performed. Some analytic results for simple special cases are presented. We also propose basic interaction mechanisms and show in simulations that these already reproduce certain particular features of prices in real stoc

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Mohamed, Linah, Mike Christie, and Vasily Demyanov. "Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification." SPE Journal 15, no. 01 (2009): 31–38. http://dx.doi.org/10.2118/119139-pa.

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Summary History matching and uncertainty quantification are two important research topics in reservoir simulation currently. In the Bayesian approach, we start with prior information about a reservoir (e.g., from analog outcrop data) and update our reservoir models with observations (e.g., from production data or time-lapse seismic). The goal of this activity is often to generate multiple models that match the history and use the models to quantify uncertainties in predictions of reservoir performance. A critical aspect of generating multiple history-matched models is the sampling algorithm us

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Полухин, V. Polukhin, Берестин, et al. "Biophysical models of pathological and postural tremor." Journal of New Medical Technologies. eJournal 9, no. 4 (2015): 0. http://dx.doi.org/10.12737/16779.

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Real chaotic and stochastic analysis of the two movements (tremor was considered as involuntary movements and tapping - arbitrary) shows them as chaotic movements (involuntary on the results of the test, not by the presence of the target). The authors introduce new criteria for separating these two types of motion in the form of matrices of pairwise comparisons of samples tremorograms and tappingrams. Identifying differences between the concrete (obtained continuously, during the sequential measurement) that are compared in pairs in one subject groups was performe

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Nejadi, Siavash, Juliana Y. Leung, Japan J. Trivedi, and Claudio Virues. "Integrated Characterization of Hydraulically Fractured Shale-Gas Reservoirs—Production History Matching." SPE Reservoir Evaluation & Engineering 18, no. 04 (2015): 481–94. http://dx.doi.org/10.2118/171664-pa.

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Summary Advancements in horizontal-well drilling and multistage hydraulic fracturing have enabled economically viable gas production from tight formations. Reservoir-simulation models play an important role in the production forecasting and field-development planning. To enhance their predictive capabilities and to capture the uncertainties in model parameters, one should calibrate stochastic reservoir models to both geologic and flow observations. In this paper, a novel approach to characterization and history matching of hydrocarbon production from a hydraulic-fractured shale is presented. T

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Bottke, William F., Richard J. Walker, James M. D. Day, David Nesvorny, and Linda Elkins-Tanton. "Stochastic Late Accretion to Earth, the Moon, and Mars." Science 330, no. 6010 (2010): 1527–30. http://dx.doi.org/10.1126/science.1196874.

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Core formation should have stripped the terrestrial, lunar, and martian mantles of highly siderophile elements (HSEs). Instead, each world has disparate, yet elevated HSE abundances. Late accretion may offer a solution, provided that ≥0.5% Earth masses of broadly chondritic planetesimals reach Earth’s mantle and that ~10 and ~1200 times less mass goes to Mars and the Moon, respectively. We show that leftover planetesimal populations dominated by massive projectiles can explain these additions, with our inferred size distribution matching those derived from the inner asteroid belt, ancient mart

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Elsheikh, Ahmed H., Mary F. Wheeler, and Ibrahim Hoteit. "Sparse calibration of subsurface flow models using nonlinear orthogonal matching pursuit and an iterative stochastic ensemble method." Advances in Water Resources 56 (June 2013): 14–26. http://dx.doi.org/10.1016/j.advwatres.2013.02.002.

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30

Qaiser Abbas. "Template Matching Based Probabilistic Optical Character Recognition for Urdu Nastaliq Script." Lahore Garrison University Research Journal of Computer Science and Information Technology 5, no. 2 (2021): 41–47. http://dx.doi.org/10.54692/lgurjcsit.2021.0502207.

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This paper presents a technique for optical recognition of Urdu characters using template matching based on a probabilistic N-Gram language model. Dataset used has the collection of both printed and typed text. This model is able to perform three types of segmentations including line, ligature and character using horizontal projection, connected component labeling, corners and pointers techniques, respectively. A separate stochastic lexicon is built from a collected corpus, which contains the probability values of grams. By using template matching and the N-Gram language model, our study predi

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31

Liu, Jian, Jiaqi Guo, Bing Hu, Qiqing Zhai, Can Tang, and Wanjia Zhang. "Controlled Symmetry with Woods-Saxon Stochastic Resonance Enabled Weak Fault Detection." Sensors 23, no. 11 (2023): 5062. http://dx.doi.org/10.3390/s23115062.

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Weak fault detection with stochastic resonance (SR) is distinct from conventional approaches in that it is a nonlinear optimal signal processing to transfer noise into the signal, resulting in a higher output SNR. Owing to this special characteristic of SR, this study develops a controlled symmetry with Woods-Saxon stochastic resonance (CSwWSSR) model based on the Woods-Saxon stochastic resonance (WSSR), where each parameter of the model may be modified to vary the potential structure. Then, the potential structure of the model is investigated in this paper, along with the mathematical analysi

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Tavakoli, Reza, Sanjay Srinivasan, and Mary F. Wheeler. "Rapid Updating of Stochastic Models by Use of an Ensemble-Filter Approach." SPE Journal 19, no. 03 (2013): 500–513. http://dx.doi.org/10.2118/163673-pa.

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Summary Applying an ensemble Kalman filter (EnKF) is an effective method for reservoir history matching. The underlying principle is that an initial ensemble of stochastic models can be progressively updated to reflect measured values as they become available. The EnKF performance is only optimal, however, if the prior-state vector is linearly related to the predicted data and if the joint distribution of the prior-state vector is multivariate Gaussian. Therefore, it is challenging to implement the filtering scheme for non-Gaussian random fields, such as channelized reservoirs, in which the co

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Liu, Mingliang, Dario Grana, and Leandro Passos de Figueiredo. "Uncertainty quantification in stochastic inversion with dimensionality reduction using variational autoencoder." GEOPHYSICS 87, no. 2 (2021): M43—M58. http://dx.doi.org/10.1190/geo2021-0138.1.

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Estimating rock and fluid properties in the subsurface from geophysical measurements is a computationally and memory-intensive inverse problem. For nonlinear problems with non-Gaussian variables, analytical solutions are generally not available, and the solutions of those inverse problems must be approximated using sampling and optimization methods. To reduce the computational cost, model and data can be reparameterized into low-dimensional spaces where the solution of the inverse problem can be computed more efficiently. Among the potential dimensionality reduction methods, deep-learning algo

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Korsunovs, Aleksandrs, Felician Campean, Gaurav Pant, Oscar Garcia-Afonso, and Efe Tunc. "Evaluation of zero-dimensional stochastic reactor modelling for a Diesel engine application." International Journal of Engine Research 21, no. 4 (2019): 592–609. http://dx.doi.org/10.1177/1468087419845823.

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Prediction of engine-out emissions with high fidelity from in-cylinder combustion simulations is still a significant challenge early in the engine development process. This article contributes to this fast evolving body of knowledge by focusing on the evaluation of NO x emission prediction capability of a probability density function–based stochastic reactor engine models for a Diesel engine. The research implements a systematic approach to the study of the stochastic reactor engine model performance, underpinned by a detailed space-filling design of experiments (DoE)-based sensitivity analysi

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Liu, Jida, and Changqi Dong. "Understanding the Complex Adaptive Characteristics of Cross-Regional Emergency Collaboration in China: A Stochastic Evolutionary Game Approach." Fractal and Fractional 8, no. 2 (2024): 98. http://dx.doi.org/10.3390/fractalfract8020098.

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Regional integration and pairing assistance are two forms of cross-regional emergency collaboration practice carried out by the Chinese government. Based on the Chinese government’s emergency management practice, evolutionary game models of cross-regional emergency collaboration were constructed. Further, the traditional evolutionary game model was improved by introducing the stochastic process, and Gaussian white noise was introduced as a random disturbance. The stochastic evolutionary game model was constructed, and the existence and stability of the equilibrium solutions of the two kinds of

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Burda, Michael C., and Mark Weder. "Complementarity of Labor Market Institutions, Equilibrium Unemployment and the Propagation of Business Cycles." German Economic Review 3, no. 1 (2002): 1–24. http://dx.doi.org/10.1111/1468-0475.00049.

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Abstract This paper evaluates complementarities of labor market institutions and the business cycle in the context of a stochastic dynamic general equilibrium model economy. Matching between workers and vacancies with endogenous time spent in search, Nash-bargained wages, payroll taxation, and differential support for unemployed labor in search and leisure are central aspects of the model. For plausible regions of the policy and institutional parameter space, the model exhibits more persistence than standard real business cycle models and can exhibit indeterminacy of rational expectations path

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Zhao, Changyuan, Hongyang Du, Guangyuan Liu, and Dusit Niyato. "Supervised Score-Based Modeling by Gradient Boosting." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22768–76. https://doi.org/10.1609/aaai.v39i21.34437.

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Score-based generative models can effectively learn the distribution of data by estimating the gradient of the distribution. Due to the multi-step denoising characteristic, researchers have recently considered combining score-based generative models with the gradient boosting algorithm, a multi-step supervised learning algorithm, to solve supervised learning tasks. However, existing generative model algorithms are often limited by the stochastic nature of the models and the long inference time, impacting prediction performances. Therefore, we propose a Supervised Score-based Model (SSM), which

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Wells, Konstans, Barry W. Brook, Robert C. Lacy, et al. "Timing and severity of immunizing diseases in rabbits is controlled by seasonal matching of host and pathogen dynamics." Journal of The Royal Society Interface 12, no. 103 (2015): 20141184. http://dx.doi.org/10.1098/rsif.2014.1184.

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Infectious diseases can exert a strong influence on the dynamics of host populations, but it remains unclear why such disease-mediated control only occurs under particular environmental conditions. We used 16 years of detailed field data on invasive European rabbits ( Oryctolagus cuniculus ) in Australia, linked to individual-based stochastic models and Bayesian approximations, to test whether (i) mortality associated with rabbit haemorrhagic disease (RHD) is driven primarily by seasonal matches/mismatches between demographic rates and epidemiological dynamics and (ii) delayed infection (arisi

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Li, Boxiao, Eric W. Bhark, (ret ). Stephen Gross, Travis C. Billiter, and Kaveh Dehghani. "Best Practices of Assisted History Matching Using Design of Experiments." SPE Journal 24, no. 04 (2019): 1435–51. http://dx.doi.org/10.2118/191699-pa.

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Summary Assisted history matching (AHM) using design of experiments (DOE) is one of the most commonly applied history-matching techniques in the oil and gas industry. When applied properly, this stochastic method finds a representative ensemble of history-matched reservoir models for probabilistic uncertainty analysis of production forecasts. Although DOE-based AHM is straightforward in concept, it can be misused in practice because the work flow involves many statistical and modeling principles that should be followed rigorously. In this paper, the entire DOE-based AHM work flow is demonstrat

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Amara-Ouali, Yvenn, Yannig Goude, Pascal Massart, Jean-Michel Poggi, and Hui Yan. "A Review of Electric Vehicle Load Open Data and Models." Energies 14, no. 8 (2021): 2233. http://dx.doi.org/10.3390/en14082233.

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The field of electric vehicle charging load modelling has been growing rapidly in the last decade. In light of the Paris Agreement, it is crucial to keep encouraging better modelling techniques for successful electric vehicle adoption. Additionally, numerous papers highlight the lack of charging station data available in order to build models that are consistent with reality. In this context, the purpose of this article is threefold. First, to provide the reader with an overview of the open datasets available and ready to be used in order to foster reproducible research in the field. Second, t

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Harrison, Jonathan U., and Christian A. Yates. "A hybrid algorithm for coupling partial differential equation and compartment-based dynamics." Journal of The Royal Society Interface 13, no. 122 (2016): 20160335. http://dx.doi.org/10.1098/rsif.2016.0335.

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Stochastic simulation methods can be applied successfully to model exact spatio-temporally resolved reaction–diffusion systems. However, in many cases, these methods can quickly become extremely computationally intensive with increasing particle numbers. An alternative description of many of these systems can be derived in the diffusive limit as a deterministic, continuum system of partial differential equations (PDEs). Although the numerical solution of such PDEs is, in general, much more efficient than the full stochastic simulation, the deterministic continuum description is generally not v

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Jin, Zixiang. "Advancements in Diffusion Models for Image Generation: A Comparative Analysis of DDPM, LDM, and DDIM." Applied and Computational Engineering 104, no. 1 (2024): 96–103. http://dx.doi.org/10.54254/2755-2721/104/20241184.

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Abstract. This research provides a thorough exploration of diffusion models in image generation, comparing various methodologies to assess their efficacy and efficiency. The study begins with an introduction to foundational technologies and key concepts, progressing through an analysis of basic and advanced models, including Latent Diffusion Models (LDMs), Denoising Diffusion Implicit Models (DDIMs), and control models. The research evaluates these models based on their performance, computational efficiency, and future development potential. The review details the evolution of diffusion models

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Saglietti, Luca, Federica Gerace, Alessandro Ingrosso, Carlo Baldassi, and Riccardo Zecchina. "From statistical inference to a differential learning rule for stochastic neural networks." Interface Focus 8, no. 6 (2018): 20180033. http://dx.doi.org/10.1098/rsfs.2018.0033.

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Stochastic neural networks are a prototypical computational device able to build a probabilistic representation of an ensemble of external stimuli. Building on the relationship between inference and learning, we derive a synaptic plasticity rule that relies only on delayed activity correlations, and that shows a number of remarkable features. Our delayed-correlations matching (DCM) rule satisfies some basic requirements for biological feasibility: finite and noisy afferent signals, Dale’s principle and asymmetry of synaptic connections, locality of the weight update computations. Nevertheless,

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Fernández Martínez, Juan Luis, Tapan Mukerji, Esperanza García Gonzalo, and Amit Suman. "Reservoir characterization and inversion uncertainty via a family of particle swarm optimizers." GEOPHYSICS 77, no. 1 (2012): M1—M16. http://dx.doi.org/10.1190/geo2011-0041.1.

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History matching provides to reservoir engineers an improved spatial distribution of physical properties to be used in forecasting the reservoir response for field management. The ill-posed character of the history-matching problem yields nonuniqueness and numerical instabilities that increase with the reservoir complexity. These features might cause local optimization methods to provide unpredictable results not being able to discriminate among the multiple models that fit the observed data (production history). Also, the high dimensionality of the inverse problem impedes estimation of uncert

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Chen, Kehua, Zhenlong Yuan, Tianlu Mao, and Zhaoqi Wang. "Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 2 (2025): 2105–13. https://doi.org/10.1609/aaai.v39i2.32208.

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The reconstruction of low-textured areas is a prominent research focus in multi-view stereo (MVS). In recent years, traditional MVS methods have performed exceptionally well in reconstructing low-textured areas by constructing plane models. However, these methods often encounter issues such as crossing object boundaries and limited perception ranges, which undermine the robustness of plane model construction. Building on previous work (APD-MVS), we propose the DPE-MVS method. By introducing dual-level precision edge information, including fine and coarse edges, we enhance the robustness of pla

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Bao, Liu, Peigen Zhang, Ze Guo, Wanqi Wang, Qing Zhu, and Yulin Ding. "Supply–Demand Matching of Engineering Construction Materials in Complex Mountainous Areas Based on Complex Environment Two-Stage Stochastic Programing." Mathematics 12, no. 17 (2024): 2683. http://dx.doi.org/10.3390/math12172683.

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Effective supply and demand matching for construction materials is a crucial challenge in large-scale railway projects, particularly in complex and hazardous environments. We propose a two-stage stochastic programing model that incorporates environmental uncertainties, such as natural disasters, into the supply chain optimization process. The first stage determines optimal locations and capacities for material supply points, while the second stage addresses material distribution under uncertain demand. We further enhance the model’s efficiency with Benders decomposition algorithm. The performa

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Sedighi, F., and K. D. D. Stephen. "Faster Convergence in Seismic History Matching by Dividing and Conquering the Unknowns." SPE Journal 15, no. 04 (2010): 1077–88. http://dx.doi.org/10.2118/121210-pa.

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Summary Seismic history matching is the process of modifying a reservoir simulation model to reproduce the observed production data in addition to information gained through time-lapse (4D) seismic data. The search for good predictions requires that many models be generated, particularly if there is an interaction between the properties that we change and their effect on the misfit to observed data. In this paper, we introduce a method of improving search efficiency by estimating such interactions and partitioning the set of unknowns into noninteracting subspaces. We use regression analysis to

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Franzese, Giulio, Simone Rossi, Lixuan Yang, et al. "How Much Is Enough? A Study on Diffusion Times in Score-Based Generative Models." Entropy 25, no. 4 (2023): 633. http://dx.doi.org/10.3390/e25040633.

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Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data. While recent works have started to lay down a theoretical foundation for these models, a detailed understanding of the role of the diffusion time T is still lacking. Current best practice advocates for a large T to ensure that the forward dynamics brings the diffusion sufficiently close to a known and simple noise distribution; however, a smaller value of T should be preferred for a better approximation of the score-matching objective and high

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Liao, Qinzhuo, Lingzao Zeng, Haibin Chang, and Dongxiao Zhang. "Efficient History Matching Using the Markov-Chain Monte Carlo Method by Means of the Transformed Adaptive Stochastic Collocation Method." SPE Journal 24, no. 04 (2019): 1468–89. http://dx.doi.org/10.2118/194488-pa.

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Summary Bayesian inference provides a convenient framework for history matching and prediction. In this framework, prior knowledge, system nonlinearity, and measurement errors can be directly incorporated into the posterior distribution of the parameters. The Markov-chain Monte Carlo (MCMC) method is a powerful tool to generate samples from the posterior distribution. However, the MCMC method usually requires a large number of forward simulations. Hence, it can be a computationally intensive task, particularly when dealing with large-scale flow and transport models. To address this issue, we c

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Kaiser, T., C. Clemen, and H. G. Maas. "AUTOMATED ALIGNMENT OF LOCAL POINT CLOUDS IN DIGITAL BUILDING MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5/W2 (September 20, 2019): 35–39. http://dx.doi.org/10.5194/isprs-archives-xlii-5-w2-35-2019.

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<p><strong>Abstract.</strong> For the correct usage and analysis within a BIM environment, image-based point clouds that were created with Structure from Motion (SfM) tools have to be transformed into the building coordinate system via a seven parameter Helmert Transformation. Usually control points are used for the estimation of the transformation parameters. In this paper we present a novel, highly automated approach to calculate these transformation parameters without the use of control points. The process relies on the relationship between wall respectively plane informat

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