When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?

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

Physics-Informed Neural Networks for Heat Transfer Problems

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

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

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

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: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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