A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations

Learning Functional Priors and Posteriors from Data and Physics

(2022) Xuhui Meng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

An Augmented Lagrangian Deep Learning Method for Variational Problems with Essential Boundary Conditions

(2022) Jianguo Huang et al.   Communications in Computational Physics

Meta-learning PINN loss functions

(2022) Apostolos F Psaros et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks

(2021) Qiming Zhu et al.   COMPUTATIONAL MECHANICS

Integro-Differential Equation for the Non-Equilibrium Thermal Response of Glass-Forming Materials: Analytical Solutions

(2021) Alexander A. Minakov et al.   Symmetry-Basel

Efficient training of physics‐informed neural networks via importance sampling

(2021) Mohammad Amin Nabian et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Physics-Informed Neural Networks for Heat Transfer Problems

(2021) Shengze Cai et al.   JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME

Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions

(2021) Yulei Liao & Pingbing Ming   Communications in Computational Physics

Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks

(2021) Colby L. Wight & Jia Zhao   Communications in Computational Physics

A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

(2021) Ehsan Haghighat et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Multi-fidelity Bayesian neural networks: Algorithms and applications

(2021) Xuhui Meng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Simulation of multi-species flow and heat transfer using physics-informed neural networks

(2021) R. Laubscher   PHYSICS OF FLUIDS

Uncovering near-wall blood flow from sparse data with physics-informed neural networks

(2021) Amirhossein Arzani et al.   PHYSICS OF FLUIDS

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

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

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

(2020) Liu Yang et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

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

Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport

(2020) QiZhi He et al.   ADVANCES IN WATER RESOURCES

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

PPINN: Parareal physics-informed neural network for time-dependent PDEs

(2020) Xuhui Meng et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Physics-informed multi-LSTM networks for metamodeling of nonlinear structures

(2020) Ruiyang Zhang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

(2020) Liu Yang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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

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

hp-VPINNs: Variational physics-informed neural networks with domain decomposition

(2020) Ehsan Kharazmi et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

(2020) Han Gao et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Adversarial uncertainty quantification in physics-informed neural networks

(2019) Yibo Yang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

(2019) Dongkun Zhang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

(2019) Ameya D. Jagtap et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics-informed neural networks for high-speed flows

(2019) Zhiping Mao et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A new algorithm for the solution of nonlinear two-dimensional Volterra integro-differential equations of high-order

(2019) Y. Wang et al.   JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

A Dynamic Analysis of Energy Storage With Renewable and Diesel Generation Using Volterra Equations

(2019) Denis Sidorov et al.   IEEE Transactions on Industrial Informatics

Transfer learning enhanced physics informed neural network for phase-field modeling of fracture

(2019) Somdatta Goswami et al.   THEORETICAL AND APPLIED FRACTURE MECHANICS

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

A sixth-order compact finite difference method for non-classical vibration analysis of nanobeams including surface stress effects

(2012) R. Ansari et al.   APPLIED MATHEMATICS AND COMPUTATION