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PythonFOAM: In-situ data analyses with OpenFOAM and Python

PythonFOAM: In-situ data analyses with OpenFOAM and Python

17 March 2021
R. Maulik
Dimitrios K. Fytanidis
Bethany Lusch
V. Vishwanath
Saumil Patel
    AI4CE
ArXivPDFHTML

Papers citing "PythonFOAM: In-situ data analyses with OpenFOAM and Python"

6 / 6 papers shown
Title
Deploying deep learning in OpenFOAM with TensorFlow
Deploying deep learning in OpenFOAM with TensorFlow
R. Maulik
Himanshu Sharma
Saumil Patel
Bethany Lusch
Elise Jennings
AI4CE
29
22
0
01 Dec 2020
Array Programming with NumPy
Array Programming with NumPy
Charles R. Harris
K. Millman
S. Walt
R. Gommers
Pauli Virtanen
...
Tyler Reddy
Warren Weckesser
Hameer Abbasi
C. Gohlke
T. Oliphant
58
14,757
0
18 Jun 2020
A Fortran-Keras Deep Learning Bridge for Scientific Computing
A Fortran-Keras Deep Learning Bridge for Scientific Computing
J. Ott
M. Pritchard
Natalie Best
Erik J. Linstead
M. Curcic
Pierre Baldi
AI4CE
23
96
0
14 Apr 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
265
42,038
0
03 Dec 2019
Deep convolutional recurrent autoencoders for learning low-dimensional
  feature dynamics of fluid systems
Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
F. J. Gonzalez
Maciej Balajewicz
AI4CE
107
139
0
03 Aug 2018
Quantifying model form uncertainty in Reynolds-averaged turbulence
  models with Bayesian deep neural networks
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
N. Geneva
N. Zabaras
BDL
32
125
0
08 Jul 2018
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