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Active Training of Physics-Informed Neural Networks to Aggregate and
  Interpolate Parametric Solutions to the Navier-Stokes Equations

Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations

2 May 2020
Christopher J. Arthurs
A. King
    PINN
ArXivPDFHTML

Papers citing "Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations"

10 / 10 papers shown
Title
An efficient hp-Variational PINNs framework for incompressible
  Navier-Stokes equations
An efficient hp-Variational PINNs framework for incompressible Navier-Stokes equations
T. Anandh
Divij Ghose
Ankit Tyagi
Abhineet Gupta
Suranjan Sarkar
Sashikumaar Ganesan
28
0
0
06 Sep 2024
Active Learning for Neural PDE Solvers
Active Learning for Neural PDE Solvers
Daniel Musekamp
Marimuthu Kalimuthu
David Holzmüller
Makoto Takamoto
Carlos Fernandez
AI4CE
41
4
0
02 Aug 2024
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for
  Machine Learning and Process-based Hydrology
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
Qingsong Xu
Yilei Shi
Jonathan Bamber
Ye Tuo
Ralf Ludwig
Xiao Xiang Zhu
AI4CE
18
9
0
08 Oct 2023
Investigating the Ability of PINNs To Solve Burgers' PDE Near
  Finite-Time BlowUp
Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp
Dibyakanti Kumar
Anirbit Mukherjee
31
2
0
08 Oct 2023
Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics
Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics
Rahul Sharma
Y.B. Guo
M. Raissi
W. Guo
PINN
AI4CE
37
5
0
23 Jul 2023
Design of Turing Systems with Physics-Informed Neural Networks
Design of Turing Systems with Physics-Informed Neural Networks
J. Kho
W. Koh
Jian Cheng Wong
P. Chiu
C. Ooi
DiffM
AI4CE
6
2
0
24 Nov 2022
Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural
  Networks on Coupled Ordinary Differential Equations
Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural Networks on Coupled Ordinary Differential Equations
Alexander New
B. Eng
A. Timm
A. Gearhart
12
4
0
14 Oct 2022
Investigation of Physics-Informed Deep Learning for the Prediction of
  Parametric, Three-Dimensional Flow Based on Boundary Data
Investigation of Physics-Informed Deep Learning for the Prediction of Parametric, Three-Dimensional Flow Based on Boundary Data
Philipp Heger
Markus Full
Daniel Hilger
N. Hosters
AI4CE
14
9
0
17 Mar 2022
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for
  Extended Domains applied to Multiphase Flow in Pipes
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes
C. Heaney
Zef Wolffs
Jón Atli Tómasson
L. Kahouadji
P. Salinas
A. Nicolle
Omar K. Matar
Ionel M. Navon
N. Srinil
Christopher C. Pain
AI4CE
24
21
0
13 Feb 2022
HyperPINN: Learning parameterized differential equations with
  physics-informed hypernetworks
HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks
Filipe de Avila Belbute-Peres
Yi-fan Chen
Fei Sha
PINN
6
38
0
28 Oct 2021
1