Self-adaptive loss balanced Physics-informed neural networks

Magnetostatics and micromagnetics with physics informed neural networks

(2022) Alexander Kovacs et al.   JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS

CoPhy -PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems

(2022) Mohannad Elhamod et al.   ACM Transactions on Intelligent Systems and Technology

A Dual-Dimer method for training physics-constrained neural networks with minimax architecture

(2021) Dehao Liu et al.   NEURAL NETWORKS

Applications of Information Channels to Physics-Informed Neural Networks for WiFi Signal Propagation Simulation at the Edge of the Industrial Internet of Things

(2021) E. Olivares et al.   NEUROCOMPUTING

When and why PINNs fail to train: A neural tangent kernel perspective

(2021) Sifan Wang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations

(2021) Enrico Schiassi et al.   NEUROCOMPUTING

Optimally weighted loss functions for solving PDEs with Neural Networks

(2021) Remco van der Meer et al.   JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

Physics-Informed Neural Networks for Cardiac Activation Mapping

(2020) Francisco Sahli Costabal et al.   Frontiers in Physics

Physics-informed neural networks for inverse problems in nano-optics and metamaterials

(2020) Yuyao Chen et al.   OPTICS EXPRESS

Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

(2020) Ameya D. Jagtap et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

DMMAN: A two-stage audio–visual fusion framework for sound separation and event localization

(2020) Ruihan Hu et al.   NEURAL NETWORKS

Distributed learning machines for solving forward and inverse problems in partial differential equations

(2020) Vikas Dwivedi et al.   NEUROCOMPUTING

Non-invasive inference of thrombus material properties with physics-informed neural networks

(2020) Minglang Yin et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

(2020) Xiaowei Jin et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Predictive large-eddy-simulation wall modeling via physics-informed neural networks

(2019) X. I. A. Yang et al.   Physical Review Fluids

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

(2019) Luning Sun et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS