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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11002675 | PMC |
| http://dx.doi.org/10.1016/j.isci.2024.109519 | DOI Listing |
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