Physics-informed machine learning
Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
Physics-informed neural networks (PINNs) for fluid mechanics: A review
Despite the significant progress over the last 50 years in simulating flow problems using
numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate …
numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate …
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Physics-informed neural networks (PINNs) have shown to be effective tools for solving both
forward and inverse problems of partial differential equations (PDEs). PINNs embed the …
forward and inverse problems of partial differential equations (PDEs). PINNs embed the …
A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data
Neural operators can learn nonlinear mappings between function spaces and offer a new
simulation paradigm for real-time prediction of complex dynamics for realistic diverse …
simulation paradigm for real-time prediction of complex dynamics for realistic diverse …
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Deep learning has been shown to be an effective tool in solving partial differential equations
(PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual …
(PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual …
Neural fields in visual computing and beyond
Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …
computing problems using methods that employ coordinate‐based neural networks. These …
Respecting causality is all you need for training physics-informed neural networks
While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this
date PINNs have not been successful in simulating dynamical systems whose solution …
date PINNs have not been successful in simulating dynamical systems whose solution …
Physics-informed neural networks for heat transfer problems
Physics-informed neural networks (PINNs) have gained popularity across different
engineering fields due to their effectiveness in solving realistic problems with noisy data and …
engineering fields due to their effectiveness in solving realistic problems with noisy data and …
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
We present the application of a class of deep learning, known as Physics Informed Neural
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …
combine data with mathematical laws in physics and engineering in a profound way …