In this paper, we investigate and design multiscale simulations for stochastic multiscale PDEs. As for the space, we consider a coarse grid and a known multiscale method, the Generalized Multiscale Finite Element Method (GMsFEM). In order to obtain a small dimensional representation of the solution in each coarse block, the uncertainty space needs to be partitioned (coarsened). This coarsening collects realizations that provide similar multiscale features as outlined in GMsFEM (or other method of choice). This step is known to be computationally demanding as it requires many local solves and clustering based on them. In this paper, we take a different approach and learn coarsening the uncertainty space. Our methods use deep learning techniques in identifying clusters(coarsening) in the uncertainty space. We use convolutional neural networks combined with some techniques in adversary neural networks. We define appropriate loss functions in the proposed neural networks, where the loss function is composed of several parts that includes terms related to clusters and reconstruction of basis functions. We present numerical results for channelized permeability fields in the examples of flows in porous media.
Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
University of Ferrara（费拉拉大学）；Italian Ministry of University and Research（意大利大学与研究部）；Royal Society（皇家学会）；European Union（欧盟）；ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing（ICSC – 高性能计算、大数据和量子计算研究中心）；Italian National Institute of High Mathematics（意大利国家高等数学研究所）
A Fuzzy-Stochastic Multiscale Model for Fiber Composites: A one-dimensional study
Institute for Computational Engineering and Sciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校计算工程与科学研究所）；Department of Mathematics and Statistics, The University of New Mexico（新墨西哥大学数学与统计系）
Nonlinear Geometric Optics Based Multiscale Stochastic Galerkin Methods for Highly Oscillatory Transport Equations with Random Inputs
Inria (IPSO team), IRMAR, University of Rennes 1（法国国家信息与自动化研究所（IPSO团队），雷恩第一大学）；Department of Mathematics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校数学系）；CNRS, IRMAR, Inria (IPSO team), University of Rennes 1（法国国家科学研究中心，法国国家信息与自动化研究所（IPSO团队），雷恩第一大学）
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs
University of Tübingen（图宾根大学）；University of Genova（热那亚大学）；Istituto Italiano di Tecnologia（意大利技术研究院）；Massachusetts Institute of Technology（麻省理工学院）；Max-Planck-Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
