31 citations · 33 across the 5 of their papers we have counts for
5 papers
Predictive Uncertainty for Neural CAE Surrogates
Kaustubh Tangsali, Mohammad Amin Nabian, Kelvin Lee +2
Neural surrogates can substantially accelerate computer-aided engineering (CAE) workflows, but their use in design requires uncertainty estimates that remain meaningful across vary…
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…
DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations
Rishikesh Ranade, Mohammad Amin Nabian, Kaustubh Tangsali +4
Numerical simulations play a critical role in design and development of engineering products and processes. Traditional computational methods, such as CFD, can provide accurate pre…
A Novel A.I Enhanced Reservoir Characterization with a Combined Mixture of Experts -- NVIDIA Modulus based Physics Informed Neural Operator Forward Model
Clement Etienam, Yang Juntao, Issam Said +4
We have developed an advanced workflow for reservoir characterization, effectively addressing the challenges of reservoir history matching through a novel approach. This method int…
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian +7
We present SimNet, an AI-driven multi-physics simulation framework, to accelerate simulations across a wide range of disciplines in science and engineering. Compared to traditional…