Linel2D-Net: A deep learning approach to solving 2D linear elastic boundary value problems on image domains.

ABSTRACT: Efficient solution of physical boundary value problems (BVPs) remains a challenging task demanded in many applications. Conventional numerical methods require time-consuming domain discretization and solving techniques that have limited throughput capabilities. Here, we present an efficient data-driven DNN approach to non-iterative solving arbitrary 2D linear elastic BVPs. Our results show that a U-Net-based surrogate model trained on a representative set of reference FDM solutions can accurately emulate linear elastic material behavior with manifold applications in deformable modeling and simulation.

SUBMITTER: Maria Antony AN 

PROVIDER: S-EPMC11002675 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature