Stephany, R., Earls, C.J. (2024) “Weak-PDE-LEARN: A weak form based approach to discovering PDEs from noisy, limited data,” Journal of Computational Physics, Vol. 506, 112950 arXiv:2309.04699


Stephany, R., Earls, C.J. (2024) “PDE-LEARN: Using deep learning to discover partial differential equations from noisy, limited data,” Neural Networks, Elsevier, Vol. 174, 106242, pp. 1-16 arXiv:2212.04971


Stephany, R., Earls, C.J. (2022) “PDE-READ: Human-readable partial differential equation discovery using deep learning,” Neural Networks, Elsevier, Vol. 154, pp. 360-382 arXiv:2111.00998

Bayesian PDE discovery

Bonneville, C., Earls, C.J. (2022) “Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data,” Journal of Computational Physics, Elsevier, Vol. 16, 100115 arXiv:2108.04085


Green’s Function Discovery:


Boullé, N., Earls, C.J., Townsend, A. (2022) “Data-driven discovery of Green’s functions with human-understandable deep learning,” Scientific Reports, Vol. 12, No. 4824, Springer Nature.


Praveen, H., Boullé, N., Earls, C.J. (2022) “Principled interpolation of Green’s functions learned from data,” IN REVIEW arXiv:2211.06299


Finite Element Analysis:

CU-BENs: 3D Nonlinear finite element structural modeling software: geometric and material nonlinear, static and transient dynamic, along with acoustic fluid-structure interaction

Download from GitHub repository

Simple CU-BEN installation guide and tutorial

“Theory manual (incomplete)” for CU-BEN: based on Cornell CEE7790 class notes