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Parameterized Neural Ordinary Differential Equations: Applications to
  Computational Physics Problems

Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems

28 October 2020
Kookjin Lee
E. Parish
ArXivPDFHTML

Papers citing "Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems"

12 / 12 papers shown
Title
When are dynamical systems learned from time series data statistically
  accurate?
When are dynamical systems learned from time series data statistically accurate?
Jeongjin Park
Nicole Yang
Nisha Chandramoorthy
AI4TS
38
4
0
09 Nov 2024
Efficient, Accurate and Stable Gradients for Neural ODEs
Efficient, Accurate and Stable Gradients for Neural ODEs
Sam McCallum
James Foster
40
4
0
15 Oct 2024
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
Julius Aka
Johannes Brunnemann
Jörg Eiden
Arne Speerforck
Lars Mikelsons
33
0
0
14 Oct 2024
Enhancing Low-Order Discontinuous Galerkin Methods with Neural Ordinary Differential Equations for Compressible Navier--Stokes Equations
Enhancing Low-Order Discontinuous Galerkin Methods with Neural Ordinary Differential Equations for Compressible Navier--Stokes Equations
Shinhoo Kang
Emil M. Constantinescu
AI4CE
22
0
0
29 Oct 2023
Do We Need an Encoder-Decoder to Model Dynamical Systems on Networks?
Do We Need an Encoder-Decoder to Model Dynamical Systems on Networks?
Bing-Quan Liu
Wei Luo
Gang Li
Jing Huang
Boxiong Yang
AI4CE
26
5
0
20 May 2023
Operator inference with roll outs for learning reduced models from
  scarce and low-quality data
Operator inference with roll outs for learning reduced models from scarce and low-quality data
W. I. Uy
D. Hartmann
Benjamin Peherstorfer
AI4CE
25
15
0
02 Dec 2022
Mining Causality from Continuous-time Dynamics Models: An Application to
  Tsunami Forecasting
Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting
Fan Wu
Sanghyun Hong
Dobsub Rim
Noseong Park
Kookjin Lee
AI4TS
31
1
0
10 Oct 2022
Parameter-varying neural ordinary differential equations with
  partition-of-unity networks
Parameter-varying neural ordinary differential equations with partition-of-unity networks
Kookjin Lee
N. Trask
27
2
0
01 Oct 2022
Constructing Neural Network-Based Models for Simulating Dynamical
  Systems
Constructing Neural Network-Based Models for Simulating Dynamical Systems
Christian Møldrup Legaard
Thomas Schranz
G. Schweiger
Ján Drgovna
Basak Falay
C. Gomes
Alexandros Iosifidis
M. Abkar
P. Larsen
PINN
AI4CE
33
93
0
02 Nov 2021
Machine learning structure preserving brackets for forecasting
  irreversible processes
Machine learning structure preserving brackets for forecasting irreversible processes
Kookjin Lee
Nathaniel Trask
P. Stinis
AI4CE
44
43
0
23 Jun 2021
Lagrangian Neural Networks
Lagrangian Neural Networks
M. Cranmer
S. Greydanus
Stephan Hoyer
Peter W. Battaglia
D. Spergel
S. Ho
PINN
139
425
0
10 Mar 2020
Symplectic Recurrent Neural Networks
Symplectic Recurrent Neural Networks
Zhengdao Chen
Jianyu Zhang
Martín Arjovsky
Léon Bottou
152
221
0
29 Sep 2019
1