2 papers
physics.flu-dyn2026
NeuralFlowNet: Towards Data-Free Physics-Informed Neural Network Solutions of Navier-Stokes Equations Across Low and High Reynolds Numbers
Jayanga T. Samarasinghe, Luis De la Fuente, Laura V. Alvarez
Physics-informed neural networks (PINNs) have emerged as a compelling pathway toward trustworthy artificial-intelligence-based computational fluid dynamics (CFD) by embedding gover…
cs.LG2025
Interpretable Machine Learning for Reservoir Water Temperatures in the U.S. Red River Basin of the South
Isabela Suaza-Sierra, Hernan A. Moreno, Luis A De la Fuente +1
Accurate prediction of Reservoir Water Temperature (RWT) is vital for sustainable water management, ecosystem health, and climate resilience. Yet, prediction alone offers limited i…