Physics-informed neural networks (PINNs) represent an emerging computational paradigm
that incorporates observed data patterns and the fundamental physical laws of a given …

In this paper, we make the first attempt to adopt the boundary integrated neural networks
(BINNs) for the numerical solution of two-dimensional (2D) elastostatic and piezoelectric …

Physics-informed deep learning (PIDL) offers innovative and powerful ways for spatio-
temporal soil consolidation analysis. However, status quo applications employ physics …

Modeling water flow in unsaturated soils is crucial in geotechnical practice. Nowadays, the
physics informed neural network (PINN) is gaining popularity in solving the Richardson …

In the present work, a physics-informed deep learning-based constitutive modeling
approach has been introduced, for the first time, to solve non-associative Drucker–Prager …

This research introduces an accelerated training approach for Vanilla Physics-Informed
Neural Networks (PINNs) that addresses three factors affecting the loss function: the initial …

Abstract Machine learning (ML) has been used to solve multiphysics problems like
thermoelasticity through multi-layer perceptron (MLP) networks. However, MLPs have high …

Neural network-based approaches have emerged as alternatives to conventional
computational fluid dynamics (CFD) in solving multiphase flow problems. However, most of …

This paper presents novel contributions to both theory and solution methodology in AI-based
analysis of solid mechanics. The physics-informed neural network (PINN) method is …

Increasing greenhouse gas levels drive extensive changes in Arctic and cold‐dominated
environments, leading to a warmer, more humid, and variable climate. Associated …