5 papers
Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks
Ali Rabeh, Suresh Murugaiyan, Adarsh Krishnamurthy +1
Fast, geometry-generalizing surrogates for unsteady flow remain challenging. We present a time-dependent, geometry-aware Deep Operator Network that predicts velocity fields for mod…
MPFBench: A Large Scale Dataset for SciML of Multi-Phase-Flows: Droplet and Bubble Dynamics
Mehdi Shadkhah, Ronak Tali, Ali Rabeh +7
Multiphase fluid dynamics, such as falling droplets and rising bubbles, are critical to many industrial applications. However, simulating these phenomena efficiently is challenging…
Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries
Ali Rabeh, Ethan Herron, Aditya Balu +4
Rapid and accurate simulations of fluid dynamics around complicated geometric bodies are critical in a variety of engineering and scientific applications, including aerodynamics an…
3D Neural Operator-Based Flow Surrogates around 3D geometries: Signed Distance Functions and Derivative Constraints
Ali Rabeh, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
Accurate modeling of fluid dynamics around complex geometries is critical for applications such as aerodynamic optimization and biomedical device design. While advancements in nume…
Modeling and simulations of high-density two-phase flows using projection-based Cahn-Hilliard Navier-Stokes equations
Ali Rabeh, Makrand A. Khanwale, John J. Lee +1
Accurately modeling the dynamics of high-density ratio () two-phase flows is important for many material science and manufacturing applications. This work consid…