Attention-enhanced neural differential equations for physics-informed deep learning of ion transport

no code implementations • 5 Dec 2023 • Danyal Rehman, John H. Lienhard

Species transport models typically combine partial differential equations (PDEs) with relations from hindered transport theory to quantify electromigrative, convective, and diffusive transport through complex nanoporous systems; however, these formulations are frequently substantial simplifications of the governing dynamics, leading to the poor generalization performance of PDE-based models.

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations

1 code implementation • NeurIPS 2023 • Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann Lecun, Bobak T. Kiani

Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering.

Representation Learning Self-Supervised Learning

Physics-constrained neural differential equations for learning multi-ionic transport

no code implementations • 7 Mar 2023 • Danyal Rehman, John H. Lienhard

In this work, we develop the first physics-informed deep learning model to learn ion transport behaviour across polyamide nanopores.