66 citations · 105 across the 4 of their papers we have counts for
4 papers
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…
From Deep to Physics-Informed Learning of Turbulence: Diagnostics
Ryan King, Oliver Hennigh, Arvind Mohan +1
We describe tests validating progress made toward acceleration and automation of hydrodynamic codes in the regime of developed turbulence by three Deep Learning (DL) Neural Network…
Automated Design using Neural Networks and Gradient Descent
Oliver Hennigh
We propose a novel method that makes use of deep neural networks and gradient decent to perform automated design on complex real world engineering tasks. Our approach works by trai…
Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks
Oliver Hennigh
Computational Fluid Dynamics (CFD) is a hugely important subject with applications in almost every engineering field, however, fluid simulations are extremely computationally and m…