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An extended deep energy method for thermo-mechanical crack propagation
Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh +4
Thermo-mechanical fracture couples transient heat conduction on a cracked domain with a crack that grows as the temperature and the displacement evolve. Neural energy solvers have…
JAXIGA - A software framework for GPU-enabled and differentiable isogeometric analysis
Cosmin Anitescu, Yongjie Jessica Zhang
In this paper, we present JAXIGA, an open-source library for isogeometric analysis based on JAX. The library separates the construction of the mesh from the computations which are…
MGRD: Compact morphology-gated residual diffusion for variance-aware cross-domain neurite forecasting
Tsung Yeh Hsieh, Cosmin Anitescu, Chunghwan Kim +2
Tracking neurite morphology over time helps characterize structural changes during neuronal development and deterioration, but long-term time-lapse imaging is resource-intensive an…
A mesh-free multiresolution deep energy method with phase-field modeling of brittle fracture
Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh +4
Phase-field modeling of brittle fracture removes the need to track cracks explicitly by recasting their evolution as the minimization of an energy functional. In return it requires…
Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
Anirudh Kalyan, Cosmin Anitescu, Xiaoying Zhuang +3
Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows. I…
Deep Energy Method with Large Language Model assistance: an open-source Streamlit-based platform for solving variational PDEs
Yizheng Wang, Cosmin Anitescu, Mohammad Sadegh Eshaghi +3
Physics-informed neural networks (PINNs) in energy form, also known as the deep energy method (DEM), offer advantages over strong-form PINNs such as lower-order derivatives and few…
Pretrain Finite Element Method: A Pretraining and Warm-start Framework for PDEs via Physics-Informed Neural Operators
Yizheng Wang, Zhongkai Hao, Mohammad Sadegh Eshaghi +4
We propose a Pretrained Finite Element Method (PFEM),a physics driven framework that bridges the efficiency of neural operator learning with the accuracy and robustness of classica…