深度学习算子网络在先进材料和工艺设计与优化中的应用

S. Koric, D. Abueidda
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引用次数: 3

摘要

摘要:本文探讨了使用新型深度算子网络(DeepONet)对数字密集型和具有挑战性的多物理场设计和先进材料和工艺优化进行正向分析的可能性。作为实现这一目标的重要一步,DeepONet网络在gpu上进行了设计和训练,以求解具有空间可变热源和高度非线性应力分布的泊松方程(热传导方程)。由于DeepONet可以学习科学和工程中各种现象和过程的参数解,因此发现经过适当训练的DeepONet可以即时准确地推断新的参数输入的热解和力学解,而无需重新训练和迁移学习,并且比经典数值方法快几个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
About Applications of Deep Learning Operator Networks for Design and Optimization of Advanced Materials and Processes
Abstract The paper explores the possibility of using the novel Deep Operator Networks (DeepONet) for forward analysis of numerically intensive and challenging multiphysics designs and optimizations of advanced materials and processes. As an important step towards that goal, DeepONet networks were devised and trained on GPUs to solve the Poisson equation (heat-conduction equation) with the spatially variable heat source and highly nonlinear stress distributions under plastic deformation with variable loads and material properties. Since DeepONet can learn the parametric solution of various phenomena and processes in science and engineering, it was found that a properly trained DeepONet can instantly and accurately inference thermal and mechanical solutions for new parametric inputs without re-training and transfer learning and several orders of magnitude faster than classical numerical methods.
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