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In this paper, we present the adaptive physics-informed neural networks(PINNs) for resolving three dimensional (3D) dynamic thermo-mechanical couplingproblems in large-size-ratio functionally graded materials (FGMs). The physicallaws described by coupled governing equations and the constraints imposed bythe initial and boundary conditions are leveraged to form the loss function ofPINNs by means of the automatic differentiation algorithm, and an adaptive lossbalancing scheme is introduced to improve the performance of PINNs. Theadaptive PINNs are meshfree and trained on batches of randomly sampledcollocation points, which is the key feature and superiority of the approach,since mesh-based methods will encounter difficulties in solving problems withlarge size ratios. The developed methodology is tested for several 3Dthermo-mechanical coupling problems in large-size-ratio FGMs, and the numericalresults demonstrate that the adaptive PINNs are effective and reliable fordealing with coupled problems in coating structures with large size ratios upto 109, as well as complex large-size-ratio geometries such as theelectrostatic comb, the airplane and the submarine.

Adaptive physics-informed neural networks for dynamic thermo-mechanical coupling problems in large-size-ratio functionally graded materials