Physics-constrained neural differential equations for learning multi-ionic transport
This addresses the challenge of modeling multi-ionic transport in nanopores for applications like desalination, but it is incremental as it builds on existing physics-informed deep learning methods.
The authors tackled the computational intractability of solving PDEs for ion transport in polyamide nanopores by developing a physics-informed deep learning model, which showed strong agreement with experimental measurements across datasets.
Continuum models for ion transport through polyamide nanopores require solving partial differential equations (PDEs) through complex pore geometries. Resolving spatiotemporal features at this length and time-scale can make solving these equations computationally intractable. In addition, mechanistic models frequently require functional relationships between ion interaction parameters under nano-confinement, which are often too challenging to measure experimentally or know a priori. In this work, we develop the first physics-informed deep learning model to learn ion transport behaviour across polyamide nanopores. The proposed architecture leverages neural differential equations in conjunction with classical closure models as inductive biases directly encoded into the neural framework. The neural differential equations are pre-trained on simulated data from continuum models and fine-tuned on independent experimental data to learn ion rejection behaviour. Gaussian noise augmentations from experimental uncertainty estimates are also introduced into the measured data to improve model generalization. Our approach is compared to other physics-informed deep learning models and shows strong agreement with experimental measurements across all studied datasets.