326 citations · 361 across the 4 of their papers we have counts for
5 papers
Physics-Informed Machine Learning and Uncertainty Quantification for Mechanics of Heterogeneous Materials
B V S S Bharadwaja, Mohammad Amin Nabian, Bharatkumar Sharma +2
In this work, a model based on the Physics - Informed Neural Networks (PINNs) for solving elastic deformation of heterogeneous solids and associated Uncertainty Quantification (UQ)…
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…
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…
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…