8 citations · 11 across the 2 of their papers we have counts for
2 papers
cs.MS2021★ 8 cited
NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations
Kirill Zubov, Zoe McCarthy, Yingbo Ma +11
Physics-informed neural networks (PINNs) are an increasingly powerful way to solve partial differential equations, generate digital twins, and create neural surrogates of physical…
cs.LG2020★ 3 cited
Physics-Informed Machine Learning Simulator for Wildfire Propagation
Luca Bottero, Francesco Calisto, Giovanni Graziano +4
The aim of this work is to evaluate the feasibility of re-implementing some key parts of the widely used Weather Research and Forecasting WRF-SFIRE simulator by replacing its core…