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Found 5 papers, 1 papers with code
Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations
This efficiently and flexibly produces a compressed representation which is used for additional conditioning of physics-informed models.
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
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.
From Deep to Physics-Informed Learning of Turbulence: Diagnostics
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.
Automated Design using Neural Networks and Gradient Descent
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.
Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks
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.
