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