Predicting peak stresses in microstructured materials using convolutional encoder–decoder learning

DOI10.1177/10812865211055504OpenAlexW4205893127MaRDI QIDQ5041623

Ankit Shrivastava, Jingxiao Liu, Kaushik Dayal, Hae Young Noh

Publication date: 14 October 2022

Published in: Mathematics and Mechanics of Solids (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2201.00722

zbMATH Keywords

microstructurespolycrystalline materialmachine learningvon Mises stressconvolutional encoder decoderpeak-stress clusters

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX)

Uses Software

• PyTorch
• Adam
• SegNet
• U-Net
• F3DAM

Cites Work

• Uncertain loading and quantifying maximum energy concentration within composite structures
• Effective response of heterogeneous materials using the recursive projection method
• Finite strain FFT-based non-linear solvers made simple
• Homogenization estimates for the average behavior and field fluctuations in cubic and hexagonal viscoplastic polycrystals
• A note on the least squares fitting of ellipses
• Unnamed Item