Physics informed neural networks (PINNs) are a novel deep learning paradigm primed for
solving forward and inverse problems of nonlinear partial differential equations (PDEs). By …

We review current and emerging knowledge-informed and brain-inspired cognitive systems
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …

In this study, we present a novel integration of numerical methodologies and advanced
computational intelligence to elucidate the dynamics of cross nanofluid flow over a Riga …

Abstract Current implementations of Physics Informed Neural Networks (PINNs) can
experience convergence problems in simulating fluid flow in porous media with highly …

Neural networks have recently gained attention in the context of solving inverse problems.
Physics-Informed Neural Networks (PINNs) are a prominent methodology for the task of …

A model based on the Physics-Informed Neural Networks (PINN) for solving elastic
deformation of heterogeneous solids and associated Uncertainty Quantification (UQ) is …

In the realm of experimental fluid mechanics, accurately reconstructing high-resolution flow
fields is notably challenging due to often sparse and incomplete data across time and space …

In the event of a fire within a tunnel, the rapid and substantial increase in temperature can
prompt swift fractures within the concrete lining. This situation can severely compromise the …

Flow modeling based on physics-informed neural networks (PINNs) is emerging as a
potential artificial intelligence (AI) technique for solving fluid dynamics problems. However …

Melt convection plays a crucial role in the growth of silicon single crystals. In particular, melt
flow transfers mass and heat, and it may strongly affect the crystal growth conditions …