3 papers
physics.geo-ph2026
AutoCF: An Automated LLM-Assisted Ecosystem for Compound Flood Simulation, Evaluation, and Impact Attribution
Soheil Radfar, Faezeh Maghsoodifar, Ning Lin +1
Compound coastal flooding (CCF) arises from interacting coastal, precipitation, and river processes, yet modeling workflows often separate simulation, evaluation, and impact analys…
physics.flu-dyn2026
How Sparse and How Noisy? Systematic Benchmarking of Inverse Physics-Informed Neural Networks for Manning Friction Estimation in Shallow Water Equations
Soheil Radfar
Physics-informed neural networks (PINNs) offer a promising framework for inverse hydrodynamic modeling by combining sparse observations with governing physical constraints. However…
physics.geo-ph2025
Integrating Newton's Laws with deep learning for enhanced physics-informed compound flood modelling
Soheil Radfar, Faezeh Maghsoodifar, Hamed Moftakhari +1
Coastal communities increasingly face compound floods, where multiple drivers like storm surge, high tide, heavy rainfall, and river discharge occur together or in sequence to prod…