Physic Informed Neural Network

Physics-informed neural networks (PINNs) integrate physical laws, typically expressed as differential equations, into neural network training to solve complex scientific problems. Current research focuses on improving PINN accuracy and efficiency through architectural innovations like Fourier-based networks, Kolmogorov-Arnold networks, and wavelet-based approaches, as well as advanced optimization strategies such as dual cone gradient descent and DiffGrad. These advancements aim to overcome limitations in handling high-frequency solutions, complex geometries, and stiff equations, ultimately enhancing the applicability of PINNs across diverse scientific and engineering domains, including fluid dynamics, seismology, and materials science.

Papers

November 22, 2022

Robustness of Physics-Informed Neural Networks to Noise in Sensor Data
Jian Cheng Wong, Pao-Hsiung Chiu, Chin Chun Ooi, My Ha Da
Native Robustness Physic Informed Neural Network Industrial Disturbing Noise Neural Network Model Sensor Data

November 21, 2022

A Curriculum-Training-Based Strategy for Distributing Collocation Points during Physics-Informed Neural Network Training
Marcus Münzer, Chris Bard
Physic Informed Neural Network Strategy Logic Collocation Point MagnetoHydroDynamics Simulation

November 17, 2022

Physics-Informed Koopman Network
Yuying Liu, Aleksei Sholokhov, Hassan Mansour, Saleh Nabi
Physic Informed Neural Network Nonlinear Dynamic Koopman Operator

November 16, 2022

November 15, 2022

Physics-informed neural networks for operator equations with stochastic data
Paul Escapil-Inchauspé, Gonzalo A. Ruz
Physic Informed Neural Network Numerical Experiment Tensor Product Operator Theory Time Dependent Sample Moment

November 14, 2022

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing
Salah A Faroughi, Nikhil Pawar, Celio Fernandes, Maziar Raissi, Subasish Das, Nima K. Kalantari, Seyed Kourosh Mahjour
Neural Network Physic Informed Neural Network Scientific Computing Multi Physic Neural Network Framework

November 10, 2022

Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi, Ravinder Bhattoo, N M Anoop Krishnan, Sayan Ranu
Graph Neural Network Physic Informed Neural Network System Performance Dynamical System Neural ODE Lagrangian Neural Network Real World System

November 8, 2022

First principles physics-informed neural network for quantum wavefunctions and eigenvalue surfaces
Marios Mattheakis, Gabriel R. Schleder, Daniel T. Larson, Efthimios Kaxiras
Physic Informed Neural Network First Principle Smallest Eigenvalue Deep Learning Solution Wave Function Eigenfunction Decomposition

November 3, 2022

Physics-informed neural networks for gravity currents reconstruction from limited data
Mickaël Delcey, Yoann Cheny, Sébastien Kiesgen de Richter
Physic Informed Neural Network Limited Data Flow Field Flow Reconstruction Unsteady Flow

November 2, 2022

Data-driven modeling of Landau damping by physics-informed neural networks
Yilan Qin, Jiayu Ma, Mingle Jiang, Chuanfei Dong, Haiyang Fu, Liang Wang, Wenjie Cheng, Yaqiu Jin
Physic Informed Neural Network Fluid Dynamic Data Driven Modeling Plasma Dynamic Landau Equation

November 1, 2022

Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
Raphaël Pellegrin, Blake Bullwinkel, Marios Mattheakis, Pavlos Protopapas
Neural Network Deep Learning Transfer Learning Physic Informed Neural Network Multi Head Fast Simulation Random Dynamical System Reactive Flow

October 25, 2022

October 24, 2022

A Novel Adaptive Causal Sampling Method for Physics-Informed Neural Networks
Jia Guo, Haifeng Wang, Chenping Hou
Physic Informed Neural Network Adaptive Sampling Numerical Experiment Collocation Point

October 23, 2022

Physic Informed Neural Network

Papers

Robustness of Physics-Informed Neural Networks to Noise in Sensor Data

A Curriculum-Training-Based Strategy for Distributing Collocation Points during Physics-Informed Neural Network Training

Physics-Informed Koopman Network

Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology

Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks

SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition

Physics-informed neural networks for operator equations with stochastic data

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing

Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems

First principles physics-informed neural network for quantum wavefunctions and eigenvalue surfaces

Physics-informed neural networks for gravity currents reconstruction from limited data

Data-driven modeling of Landau damping by physics-informed neural networks

Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows

FO-PINNs: A First-Order formulation for Physics Informed Neural Networks

SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain

Deep nurbs -- admissible neural networks

Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano-particles in a contaminated aquifer

A Novel Adaptive Causal Sampling Method for Physics-Informed Neural Networks

Less Emphasis on Difficult Layer Regions: Curriculum Learning for Singularly Perturbed Convection-Diffusion-Reaction Problems

Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks