1. A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features
- Author
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Vasilis Krokos, Stéphane Bordas, Viet Bui Xuan, Pierre Kerfriden, Philippe Young, Cardiff University, Synopsys Northern Europe, University of Luxembourg [Luxembourg], Cardiff University (Cardiff University), Centre des Matériaux (MAT), MINES ParisTech - École nationale supérieure des mines de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer science ,Bayesian probability ,Computational Mechanics ,Direct numerical simulation ,Ocean Engineering ,010103 numerical & computational mathematics ,01 natural sciences ,Convolutional neural network ,Machine Learning (cs.LG) ,Computational Engineering, Finance, and Science (cs.CE) ,Stress (mechanics) ,[PHYS.MECA.STRU]Physics [physics]/Mechanics [physics]/Structural mechanics [physics.class-ph] ,Boundary value problem ,0101 mathematics ,Computer Science - Computational Engineering, Finance, and Science ,Microscale chemistry ,Applied Mathematics ,Mechanical Engineering ,Elasticity (physics) ,Finite element method ,010101 applied mathematics ,Computational Mathematics ,Computational Theory and Mathematics ,Algorithm - Abstract
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., Comment: 50 pages, 42 figures; corrected typos; clarifications
- Published
- 2021