Discovering governing partial differential equations from noisy data

Neural ordinary differential equations with irregular and noisy data

(2023) Pawan Goyal et al.   Royal Society Open Science

Integration of machine learning and first principles models

(2022) Lokesh Rajulapati et al.   AICHE JOURNAL

A hybrid modelling approach to model process dynamics by the discovery of a system of partial differential equations

(2022) Kiran Raviprakash et al.   COMPUTERS & CHEMICAL ENGINEERING

Recent trends on hybrid modeling for Industry 4.0

(2021) Joel Sansana et al.   COMPUTERS & CHEMICAL ENGINEERING

AI Feynman: A physics-inspired method for symbolic regression

(2020) Silviu-Marian Udrescu et al.   Science Advances

DeepMoD: Deep learning for model discovery in noisy data

(2020) Gert-Jan Both et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data-driven discovery of partial differential equations

(2017) Samuel H. Rudy et al.   Science Advances

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE