physics-informed-neural-network

This paper introduces PCINN, a physics-chemistry-informed neural network that acts as a hybrid AI surrogate for real-time spatial atomic layer deposition (SALD) coverage prediction, achieving CFD-level accuracy in ~77ms and enabling reliable kinetics inversion via identifiability analysis.

Proposes a closed-loop evolutionary algorithm that guides LLMs to generate complete, executable PINN configurations, reducing mean-squared error on a one-dimensional multiscale wave equation.