nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling
Abstract
A series of physics-informed neural networks for prediction of time- and rate-dependent material functions in non-Newtonian fluids in response to different deformation fields are presented and rigorously interrogated against conventional CFD solutions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2022
- Source ID
- 10.1039/d1sm01298c
Entities
People
- George Karniadakis
- Mohammadamin Mahmoudabadbozchelou
- Safa Jamali
Organizations
- Brown University
- Northeastern University
- United States Department of Energy