A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features
Cardiff University（卡迪夫大学）；Synopsys Northern Europe Ltd（新思科技北欧有限公司）；University of Luxembourg（卢森堡大学）；University of Exeter（埃克塞特大学）；Mines ParisTech / PSL University（巴黎高科矿业学院 / 巴黎科学与文学大学）
Vasilis Krokos, Viet Bui Xuan, Stéphane P. A. Bordas, Philippe Young, Pierre Kerfriden
Multiscale computational modelling is challenging due to the high computational cost of direct numerical simulation by finite elements. To address this issue, concurrent multiscale methods use the solution of cheaper macroscale surrogates as boundary conditions to microscale sliding windows. The microscale problems remain a numerically challenging operation both in terms of implementation and cost. In this work we propose to replace the local microscale solution by an Encoder-Decoder Convolutional Neural Network that will generate fine-scale stress corrections to coarse predictions around unresolved microscale features, without prior parametrisation of local microscale problems. We deploy a Bayesian approach providing credible intervals to evaluate the uncertainty of the predictions, which is then used to investigate the merits of a selective learning framework. We will demonstrate the capability of the approach to predict equivalent stress fields in porous structures using linearised and finite strain elasticity theories.
A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials
Institute for Software and Systems Engineering, Clausthal University of Technology, Germany（1 软件与系统工程学院， Clausthal 技术大学）；Institute of Applied Mechanics, Clausthal University of Technology, Germany（2 应用力学学院， Clausthal 技术大学）
Multiscale Structural Reliability Analysis in high dimensions with Tensor Trains and Physics-Augmented Neural Networks
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Sydney（悉尼大学）；The Oden Institute of Computational Science and Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校奥登计算科学与工程研究所）
