236 citations · 724 across the 10 of their papers we have counts for
5 papers · 1 filter
An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data
Bahador Bahmani, WaiChing Sun
We present a hybrid model/model-free data-driven approach to solve poroelasticity problems. Extending the data-driven modeling framework originated from Kirchdoerfer and Ortiz (201…
Sobolev training of thermodynamic-informed neural networks for smoothed elasto-plasticity models with level set hardening
Nikolaos N. Vlassis, WaiChing Sun
We introduce a deep learning framework designed to train smoothed elastoplasticity models with interpretable components, such as a smoothed stored elastic energy function, a yield…
Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity
Nikolaos Vlassis, Ran Ma, WaiChing Sun
This paper is the first attempt to use geometric deep learning and Sobolev training to incorporate non-Euclidean microstructural data such that anisotropic hyperelastic material ma…
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation
Kun Wang, WaiChing Sun, Qiang Du
We introduce a multi-agent meta-modeling game to generate data, knowledge, and models that make predictions on constitutive responses of elasto-plastic materials. We introduce a ne…
Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning
Kun Wang, WaiChing Sun
This paper presents a new meta-modeling framework to employ deep reinforcement learning (DRL) to generate mechanical constitutive models for interfaces. The constitutive models are…