Journal of Machine Learning for Modeling and Computing最新文献_第3页

An Application of Machine Learning to College Admissions: The Summer Melt Problem 机器学习在大学录取中的应用:夏季融化问题

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2022046289

Treena S. Basu, R. Buckmire, Osei K. Tweneboah

引用次数: 0

Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression 基于集成高斯过程回归的电力负荷与功率预测

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2022041871

Tong Ma, D. Barajas-Solano, Renke Huang, A. Tartakovsky

引用次数: 0

A Spatiotemporal-Aware Weighting Scheme for Improving Climate Model Ensemble Predictions 改进气候模式集合预测的时空感知加权方案

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2022046715

Ming Fan, Dawei Lu, D. Rastogi, Eric Pierce

引用次数: 2

Using Physics-Informed Neural Networks to Solve for Permeability Field under Two-Phase Flow in Heterogeneous Porous Media 基于物理信息的神经网络求解非均质多孔介质两相流渗透率场

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2023046921

Mingyuan Yang, John Foster

{"title":"Using Physics-Informed Neural Networks to Solve for Permeability Field under Two-Phase Flow in Heterogeneous Porous Media","authors":"Mingyuan Yang, John Foster","doi":"10.1615/jmachlearnmodelcomput.2023046921","DOIUrl":"https://doi.org/10.1615/jmachlearnmodelcomput.2023046921","url":null,"abstract":"Physics-informed neural networks (PINNs) have recently been applied to a wide range of computational physical problems. In this paper, we use PINNs to solve an inverse two-phase ﬂow problem in heterogeneous porous media where only sparse direct and indirect measurements are available. The forward two-phase ﬂow problem is governed by a coupled system of partial differential equations (PDEs) with initial and boundary conditions. As for inverse problems, the solutions are assumed to be known at scattered locations but some coefﬁcients or variable functions in the PDEs are missing or incomplete. The idea is to train multiple neural networks representing the solutions and the unknown variable function at the same time such that both the underlying physical laws and the measurements can be honored. The numerical results show that our proposed method is able to recover the incomplete permeability ﬁeld in different scenarios. Moreover, we show that the method can be used to forecast the future dynamics with the same format of loss function formulation. In addition, we employ a neural network structure inspired by the deep operator networks (DeepONets) to represent the solutions which can potentially shorten the time of the training process.","PeriodicalId":320604,"journal":{"name":"Journal of Machine Learning for Modeling and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129197583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

MACHINE LEARNING FOR TRAJECTORIES OF PARAMETRIC NONLINEAR DYNAMICAL SYSTEMS 参数非线性动力系统轨迹的机器学习

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2020034093

R. Pulch, Maha Youssef

{"title":"MACHINE LEARNING FOR TRAJECTORIES OF PARAMETRIC NONLINEAR DYNAMICAL SYSTEMS","authors":"R. Pulch, Maha Youssef","doi":"10.1615/jmachlearnmodelcomput.2020034093","DOIUrl":"https://doi.org/10.1615/jmachlearnmodelcomput.2020034093","url":null,"abstract":"We investigate parameter-dependent nonlinear dynamical systems consisting of ordinary differential equations or differential-algebraic equations. A single quantity of interest is observed, which depends on the solution of a system. Our aim is to determine efficient approximations of the trajectories belonging to the quantity of interest in the time domain. We arrange a set of samples including trajectories of this quantity. A proper orthogonal decomposition of this data yields a reduced basis. Consequently, the mapping from the parameter domain to the basis coefficients is approximated. We apply machine learning with artificial neural networks for this approximation, where the degrees of freedom are fitted to the data of the sample trajectories in a nonlinear optimization. Alternatively, we consider a polynomial approximation, which is identified by regression, for comparison. Furthermore, concepts of sensitivity analysis are examined to characterize the impact of an input parameter on the output of the exact mapping or the approximations from the neural networks. We present results of numerical computations for examples of nonlinear dynamical systems.","PeriodicalId":320604,"journal":{"name":"Journal of Machine Learning for Modeling and Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123001290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

CONTENTS, VOLUME 1, 2020 目录，2020年第1卷

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.v1.i2.40

引用次数: 0

Inverse analysis with variational autoencoders: a comparison of shallow and deep networks 变分自编码器的逆分析:浅网络和深网络的比较

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2022042093

Hao Wu, Daniel O’Malley, John K. Golden, Velimir Vesselinov

{"title":"Inverse analysis with variational autoencoders: a comparison of shallow and deep networks","authors":"Hao Wu, Daniel O’Malley, John K. Golden, Velimir Vesselinov","doi":"10.1615/jmachlearnmodelcomput.2022042093","DOIUrl":"https://doi.org/10.1615/jmachlearnmodelcomput.2022042093","url":null,"abstract":"Inverse problems are applied to determine unknown properties by matching observational data with a physical model that often takes many parameters as input. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented involving regularization with a technique known as a variational autoencoder (VAE), which is trained to map a high-dimensional parameter space with a complex structure to a low-dimensional latent space with a simple structure. We apply this approach to unconditioned realizations of the parameters (hetero-geneous hydraulic ﬁelds) for a hydrogeological inverse problem. Two types of hydraulic conductivity ﬁelds are used to evaluate the characterization for the different levels of heterogeneity complexity of the physical inputs. This approach keeps the computational cost of generating the training data low. The reason is unconditioned realizations neither rely on the observational data used to perform the inverse analysis nor require any groundwater ﬂow model forward runs. In addition, this approach applies regularization on a low-dimensional latent space from the VAE and increases optimization efﬁciency through automatic differentiation. Furthermore, two different neural network (NN) structures are tested for their utility in using the VAE for inverse analysis. The performance of a deep, convolutional neural network strongly depends on the dimensionality of the latent space, which requires tuning. In contrast, a shallow, dense neural network provides consistently accurate characterization without tuning. Our approach evaluates the advantages of the shallow, dense neural network over the deep, convolutional one and enables future application to a wide range of inverse problems.","PeriodicalId":320604,"journal":{"name":"Journal of Machine Learning for Modeling and Computing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126986031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Assessing ChatGPT for coding finite element methods 评估ChatGPT编码有限元方法

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2023049326

Giuseppe Orlando

引用次数: 1

A NOTE ON APPLICABILITY OF ARTIFICIAL INTELLIGENCE TO CONSTITUTIVE MODELING OF GEOMATERIALS 关于人工智能在岩土材料本构建模中的适用性的说明

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2020036318

M. Pouragha, M. Eghbalian, R. Wan

引用次数: 2

NeuralGene: Inferring gene regulation and cell fate dynamics from Neural ODEs 神经基因:从神经ode推断基因调控和细胞命运动力学

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2023047369

Yutong Sha, Yuchi Qiu, Qing Nie

引用次数: 0