326 citations · 749 across the 18 of their papers we have counts for
16 papers · 1 filter
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…
High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention
Deepak Akhare, Mohammad Amin Nabian, Corey Adams +2
Automotive crashworthiness optimization remains a safety-critical challenge, requiring the management of large-scale nonlinear structural deformations and energy dissipation throug…
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…
MoWE : A Mixture of Weather Experts
Dibyajyoti Chakraborty, Romit Maulik, Peter Harrington +3
Data-driven weather models have recently achieved state-of-the-art performance, yet progress has plateaued in recent years. This paper introduces a Mixture of Experts (MoWE) approa…
A Mixture of Experts Gating Network for Enhanced Surrogate Modeling in External Aerodynamics
Mohammad Amin Nabian, Sanjay Choudhry
The computational cost associated with high-fidelity CFD simulations remains a significant bottleneck in the automotive design and optimization cycle. While ML-based surrogate mode…
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…
X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation
Mohammad Amin Nabian, Chang Liu, Rishikesh Ranade +1
Graph Neural Networks (GNNs) have gained significant traction for simulating complex physical systems, with models like MeshGraphNet demonstrating strong performance on unstructure…
Virtual Foundry Graphnet for Metal Sintering Deformation Prediction
Rachel, Chen, Juheon Lee +4
Metal Sintering is a necessary step for Metal Injection Molded parts and binder jet such as HP's metal 3D printer. The metal sintering process introduces large deformation varying…
Geometry-Informed Neural Operator for Large-Scale 3D PDEs
Zongyi Li, Nikola Borislavov Kovachki, Chris Choy +8
We propose the geometry-informed neural operator (GINO), a highly efficient approach to learning the solution operator of large-scale partial differential equations with varying ge…
FO-PINNs: A First-Order formulation for Physics Informed Neural Networks
Rini J. Gladstone, Mohammad A. Nabian, N. Sukumar +2
Physics-Informed Neural Networks (PINNs) are a class of deep learning neural networks that learn the response of a physical system without any simulation data, and only by incorpor…
Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders
Rini Jasmine Gladstone, Mohammad Amin Nabian, Vahid Keshavarzzadeh +1
Robust topology optimization (RTO), as a class of topology optimization problems, identifies a design with the best average performance while reducing the response sensitivity to i…
Efficient training of physics-informed neural networks via importance sampling
Mohammad Amin Nabian, Rini Jasmine Gladstone, Hadi Meidani
Physics-Informed Neural Networks (PINNs) are a class of deep neural networks that are trained, using automatic differentiation, to compute the response of systems governed by parti…
Adaptive Physics-Informed Neural Networks for Markov-Chain Monte Carlo
Mohammad Amin Nabian, Hadi Meidani
In this paper, we propose the Adaptive Physics-Informed Neural Networks (APINNs) for accurate and efficient simulation-free Bayesian parameter estimation via Markov-Chain Monte Car…
Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis
Mohammad Amin Nabian, Hadi Meidani
In this paper, we introduce a physics-driven regularization method for training of deep neural networks (DNNs) for use in engineering design and analysis problems. In particular, w…
A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations
Mohammad Amin Nabian, Hadi Meidani
Developing efficient numerical algorithms for the solution of high dimensional random Partial Differential Equations (PDEs) has been a challenging task due to the well-known curse…