Millionfold accelerated AI solver for 3D multi-physical simulations of ultrapermeable membranes
Abstract Solving three-dimensional (3D) multi-physics forward and inverse problems is indispensable for fundamental understanding and optimal design of membrane-based desalination systems. Unfortunately, it is computationally expensive when applying traditional numerical methods. Herein, a modified...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | npj Clean Water |
| Online Access: | https://doi.org/10.1038/s41545-025-00491-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Solving three-dimensional (3D) multi-physics forward and inverse problems is indispensable for fundamental understanding and optimal design of membrane-based desalination systems. Unfortunately, it is computationally expensive when applying traditional numerical methods. Herein, a modified Fourier neural operator (FNO)-based method is proposed to efficiently solve complex 3D multi-physics problems. The intelligent solver solves the 3D forward problems in seconds, which is approximately 105-106 times faster than traditional finite-element based method with a comparable solution quality. The average prediction accuracy is more than 96%. Moreover, the proposed FNO-based method is mesh-independent and has zero-shot super-resolution ability. It can be used to provide a fast solution for the optimal design of membrane module to mitigate concentration polarization and membrane fouling for next-generation ultrapermeable membrane system. |
|---|---|
| ISSN: | 2059-7037 |