no code implementations • 24 Feb 2022 • Benjamin Wu, Oliver Hennigh, Jan Kautz, Sanjay Choudhry, Wonmin Byeon
This efficiently and flexibly produces a compressed representation which is used for additional conditioning of physics-informed models.
no code implementations • 14 Dec 2020 • Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian, Akshay Subramaniam, Kaustubh Tangsali, Max Rietmann, Jose del Aguila Ferrandis, Wonmin Byeon, Zhiwei Fang, Sanjay Choudhry
We present real-world use cases that range from challenging forward multi-physics simulations with turbulence and complex 3D geometries, to industrial design optimization and inverse problems that are not addressed efficiently by the traditional solvers.
no code implementations • 16 Oct 2018 • Ryan King, Oliver Hennigh, Arvind Mohan, Michael Chertkov
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 (NN) schemes trained on Direct Numerical Simulations of turbulence.
1 code implementation • ICLR 2018 • Oliver Hennigh
Our approach works by training a neural network to mimic the fitness function of a design optimization task and then, using the differential nature of the neural network, perform gradient decent to maximize the fitness.
no code implementations • 25 May 2017 • 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 memory demanding.