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Found 3 papers, 1 papers with code
Physics-Informed Diffusion Models
We present a framework to inform denoising diffusion models on underlying constraints on such generated samples during model training.
GNN-Assisted Phase Space Integration with Application to Atomistics
As a remedy, we demonstrate that Graph Neural Networks, trained on Monte-Carlo data, can serve as a replacement for commonly used numerical quadrature rules, overcoming their deficiencies and significantly improving the accuracy.
Physics-Informed Neural Networks for Shell Structures
The numerical modeling of thin shell structures is a challenge, which has been met by a variety of finite element (FE) and other formulations -- many of which give rise to new challenges, from complex implementations to artificial locking.
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