9 papers · 1 filter
GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer
Corey Adams, Rishikesh Ranade, Ram Cherukuri +1
We present GeoTransolver, a multiscale geometry-aware physics attention transformer for Computer Aided Engineering (CAE). GeoTransolver extends the Transolver backbone with GALE (G…
Automotive Crash Dynamics Modeling Accelerated with Machine Learning
Mohammad Amin Nabian, Sudeep Chavare, Deepak Akhare +3
Crashworthiness assessment is a critical aspect of automotive design, traditionally relying on high-fidelity finite element (FE) simulations that are computationally expensive and…
A Benchmarking Framework for AI models in Automotive Aerodynamics
Kaustubh Tangsali, Rishikesh Ranade, Mohammad Amin Nabian +5
In this paper, we introduce a benchmarking framework within the open-source NVIDIA PhysicsNeMo-CFD framework designed to systematically assess the accuracy, performance, scalabilit…
Accelerating Transient CFD through Machine Learning-Based Flow Initialization
Peter Sharpe, Rishikesh Ranade, Kaustubh Tangsali +3
Transient computational fluid dynamics (CFD) simulations are essential for many industrial applications, but suffer from high compute costs relative to steady-state simulations. Th…
Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem
Youngkyu Lee, Shanqing Liu, Zongren Zou +5
Three-dimensional target identification using scattering techniques requires high accuracy solutions and very fast computations for real-time predictions in some critical applicati…
A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations
Sheel Nidhan, Haoliang Jiang, Lalit Ghule +3
In this paper, we propose a domain-decomposition-based deep learning (DL) framework, named transient-CoMLSim, for accurately modeling unsteady and nonlinear partial differential eq…