A Review of Physics Informed Neural Networks for Multiscale Analysis and Inverse Problems

Removing 65 Years of Approximation in Rotating Ring Disk Electrode Theory with Physics-Informed Neural Networks

“…The recent introduction of PINN, as a novel discretization-free partial differential equation (PDE) solver, has proven effective and accurate, to solve complicated PDEs in various domains, from modeling and reconstructing fluid mechanics flow fields, , to material fatigue prediction and solid mechanics, , and to blood pressure and hemodynamics estimation in healthcare. , In the field of electrochemistry, PINN has re-educated hydrodynamic electrochemistry simulation in areas ranging from single and double microband channel electrodes to the rotating disk electrode with analytical levels of accuracy. , In 2024, PINN is no longer at its infancy, or is complementary to traditional finite difference and finite element methods . The Electrochemistry-Informed Neural Netwok (ECINN) embedded electrochemical kinetic laws with mass transport equations, achieving simultaneous discovery of electrochemical rate constants, transfer coefficients, and diffusion coefficients .…”

“… 15 , 27 In 2024, PINN is no longer at its infancy, or is complementary to traditional finite difference and finite element methods. 28 The Electrochemistry-Informed Neural Netwok (ECINN) embedded electrochemical kinetic laws with mass transport equations, achieving simultaneous discovery of electrochemical rate constants, transfer coefficients, and diffusion coefficients. 29 PINN stands ready to solve electrochemical problems for the community, offering freedom from previously essential approximations both physical and mathematical.…”