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

Anto Nivin Maria Antony; Narendra Narisetti; Evgeny Gladilin

doi:10.1016/j.isci.2024.109519

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

iScience. 2024 Mar 18;27(4):109519. doi: 10.1016/j.isci.2024.109519. eCollection 2024 Apr 19.

Authors

Anto Nivin Maria Antony¹, Narendra Narisetti¹, Evgeny Gladilin¹

Affiliation

¹ Leibniz Institute of Plant Genetics and Crop Plant Research, OT Gatersleben, Corrensstr. 3, 06466 Seeland, Germany.

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.

Keywords: Computer science; Natural sciences; Physics.