Abstract: An energy-based a posteriori error bound is proposed for the physics-informed neural network solutions of elasticity problems. An admissible displacement-stress solution pair is obtained from a mixed form of physics-informed ...

Abstract: Physics-informed neural networks (PINNs) have received increased interest for forward, inverse, and surrogate modeling of problems described by partial differential equations (PDEs). However, their application to multiphysics ...

Abstract: We explore an application of the Physics-Informed Neural Networks (PINNs) in conjunction with Airy stress functions and Fourier series to find optimal solutions to a few reference biharmonic problems of elasticity and ...