Investigation of Physics-Informed Deep Learning for the Prediction of Parametric, Three-Dimensional Flow Based on Boundary Data

17 March 2022

Papers citing "Investigation of Physics-Informed Deep Learning for the Prediction of Parametric, Three-Dimensional Flow Based on Boundary Data"

8 / 8 papers shown

Title
An extended physics informed neural network for preliminary analysis of parametric optimal control problems N. Demo M. Strazzullo G. Rozza PINN 46 34 0 26 Oct 2021
Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations Christopher J. Arthurs A. King PINN 129 52 0 02 May 2020
Physics-informed deep learning for incompressible laminar flows Chengping Rao Hao Sun Yang Liu PINN AI4CE 101 223 0 24 Feb 2020
Towards Physics-informed Deep Learning for Turbulent Flow Prediction Rui Wang K. Kashinath M. Mustafa A. Albert Rose Yu PINN AI4CE 39 365 0 20 Nov 2019
Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks Günther Waxenegger-Wilfing Kai Dresia J. Deeken M. Oschwald 27 30 0 24 Jul 2019
Artificial Neural Network Surrogate Modeling of Oil Reservoir: a Case Study O. Sudakov D. Koroteev B. Belozerov Evgeny Burnaev 21 9 0 20 May 2019
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 156 2,800 0 20 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.7K 150,006 0 22 Dec 2014