Search Results for author:
Found 5 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.
Nonequilibrium thermomechanics of Gaussian phase packet crystals: application to the quasistatic quasicontinuum method
We investigate the quasistatics and dynamics of a crystalline solid described as a lattice of lumped correlated Gaussian phase packets occupying atomic lattice sites.
Mesoscale and Nanoscale Physics Atomic Physics Classical Physics Computational Physics
Data-driven topology optimization of spinodoid metamaterials with seamlessly tunable anisotropy
We present a two-scale topology optimization framework for the design of macroscopic bodies with an optimized elastic response, which is achieved by means of a spatially-variant cellular architecture on the microscale.
Computational Engineering, Finance, and Science
