University of Amsterdam, Amsterdam, The Netherlands（阿姆斯特丹大学）；Skolkovo Institute of Science and Technology（斯克沃尔诺夫科学与技术研究所）；Institute of Numerical Mathematics RAS, Moscow, Russia（俄罗斯科学院数值数学研究所）
In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty of this work is to state the problem as an image segmentation task. We leverage the power of deep learning methods as the efficient pixel-wise image labeling technique to perform the topology optimization. We introduce convolutional encoder-decoder architecture and the overall approach of solving the above-described problem with high performance. The conducted experiments demonstrate the significant acceleration of the optimization process. The proposed approach has excellent generalization properties. We demonstrate the ability of the application of the proposed model to other problems. The successful results, as well as the drawbacks of the current method, are discussed.
Consistent machine learning for topology optimization with microstructure-dependent neural network material models
Delft University of Technology（代尔夫特理工大学）；Princeton University（普林斯顿大学）；Princeton Materials Institute (PMI), Princeton University（普林斯顿材料研究所（PMI），普林斯顿大学）；Brown University（布朗大学）
Neural Networks for Generating Better Local Optima in Topology Optimization
Chair of Computational Modeling and Simulation, Technical University of Munich, School of Engineering and Design, Arcisstraße 21, Munich, 80 333, Germany（慕尼黑技术大学计算建模与模拟系）；Department of Civil and Mechanical Engineering, Technical University of Denmark（丹麦技术大学土木与机械工程系）；BMW Group, Knorrstraße 147, 80937 München（宝马集团）；Chair of Data Science in Engineering, Bauhaus-Universität Weimar, Coudraystraße 13 b, 99423, Weimar, Germany（魏玛包豪斯大学工程数据科学系）
Simplifying deflation for non-convex optimization with applications in Bayesian inference and topology optimization
Pumas-AI Inc.（Pumas-AI公司）；The University of Sydney Business School（悉尼大学商学院）；Department of Architecture, Massachusetts Institute of Technology（麻省理工学院建筑系（注：因要求机构粒度到大学级别，此处实际机构应为麻省理工学院））
