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This paper presents the potential of applying physics-informed neuralnetworks for solving nonlinear multiphysics problems, which are essential tomany fields such as biomedical engineering, earthquake prediction, andunderground energy harvesting. Specifically, we investigate how to extend themethodology of physics-informed neural networks to solve both the forward andinverse problems in relation to the nonlinear diffusivity and Biot's equations.We explore the accuracy of the physics-informed neural networks with differenttraining example sizes and choices of hyperparameters. The impacts of thestochastic variations between various training realizations are alsoinvestigated. In the inverse case, we also study the effects of noisymeasurements. Furthermore, we address the challenge of selecting thehyperparameters of the inverse model and illustrate how this challenge islinked to the hyperparameters selection performed for the forward one.

Physics-informed Neural Networks for Solving Nonlinear Diffusivity and Biot's equations