Higher-order multi-scale physics-informed randomized neural network method for efficient and high-accuracy simulation of dynamic thermo-mechanical coupling problems
Mathematics and Mechanics of Solids
Published online on September 15, 2025
Abstract
Mathematics and Mechanics of Solids, Ahead of Print.
Deep learning methods encounter significantly low-efficiency and low-accuracy challenges in effectively computing multi-scale multi-physics problems. In this study, an innovative higher-order multi-scale physics-informed randomized neural network (HOMS-...
Deep learning methods encounter significantly low-efficiency and low-accuracy challenges in effectively computing multi-scale multi-physics problems. In this study, an innovative higher-order multi-scale physics-informed randomized neural network (HOMS-...