Physics-informed neural networks for inverse problems in supersonic flows

Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre–Green–Naghdi equations

(2022) Ameya D. Jagtap et al.   OCEAN ENGINEERING

Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks

(2021) Shengze Cai et al.   JOURNAL OF FLUID MECHANICS

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

A data-driven physics-informed finite-volume scheme for nonclassical undercompressive shocks

(2021) Deniz A. Bezgin et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems

(2021) Khemraj Shukla et al.   IEEE SIGNAL PROCESSING MAGAZINE

Thermodynamically consistent physics-informed neural networks for hyperbolic systems

(2021) Ravi G. Patel et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Parallel physics-informed neural networks via domain decomposition

(2021) Khemraj Shukla et al.   JOURNAL OF COMPUTATIONAL PHYSICS

DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators

(2021) Zhiping Mao et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

(2020) Maziar Raissi et al.   SCIENCE

Inverting shock-wave temperatures via artificial neural networks

(2020) Zhiyu He et al.   JOURNAL OF APPLIED PHYSICS

Constraint-aware neural networks for Riemann problems

(2020) Jim Magiera et al.   JOURNAL OF COMPUTATIONAL 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

Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

(2020) Ameya D. Jagtap et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

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

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

Adversarial uncertainty quantification in physics-informed neural networks

(2019) Yibo Yang 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

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