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Apr 19, 2024;27 (4): 109519.

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
Author Information
  1. Anto Nivin Maria Antony: Leibniz Institute of Plant Genetics and Crop Plant Research, OT Gatersleben, Corrensstr. 3, 06466 Seeland, Germany.
  2. Narendra Narisetti: Leibniz Institute of Plant Genetics and Crop Plant Research, OT Gatersleben, Corrensstr. 3, 06466 Seeland, Germany.
  3. Evgeny Gladilin: Leibniz Institute of Plant Genetics and Crop Plant Research, OT Gatersleben, Corrensstr. 3, 06466 Seeland, Germany.
PMID: 38595795 DOI: 10.1016/j.isci.2024.109519

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

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Journal Article

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