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Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics
  and Extract Noise Probability Distributions from Data

Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data

12 September 2020
Kadierdan Kaheman
Steven L. Brunton
J. Nathan Kutz
ArXivPDFHTML

Papers citing "Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data"

9 / 9 papers shown
Title
Learning noise-induced transitions by multi-scaling reservoir computing
Learning noise-induced transitions by multi-scaling reservoir computing
Zequn Lin
Zhaofan Lu
Zengru Di
Ying Tang
33
4
0
11 Sep 2023
Discovering Causal Relations and Equations from Data
Discovering Causal Relations and Equations from Data
Gustau Camps-Valls
Andreas Gerhardus
Urmi Ninad
Gherardo Varando
Georg Martius
E. Balaguer-Ballester
Ricardo Vinuesa
Emiliano Díaz
L. Zanna
Jakob Runge
PINN
AI4Cl
AI4CE
CML
40
73
0
21 May 2023
Multiphysics discovery with moving boundaries using Ensemble SINDy and
  Peridynamic Differential Operator
Multiphysics discovery with moving boundaries using Ensemble SINDy and Peridynamic Differential Operator
A. Bekar
E. Haghighat
E. Madenci
AI4CE
24
2
0
27 Mar 2023
Investigating Sindy As a Tool For Causal Discovery In Time Series
  Signals
Investigating Sindy As a Tool For Causal Discovery In Time Series Signals
Andrew O'Brien
Rosina O. Weber
Edward J. Kim
CML
35
6
0
29 Dec 2022
Discrepancy Modeling Framework: Learning missing physics, modeling
  systematic residuals, and disambiguating between deterministic and random
  effects
Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects
Megan R. Ebers
K. Steele
J. Nathan Kutz
37
15
0
10 Mar 2022
A Priori Denoising Strategies for Sparse Identification of Nonlinear
  Dynamical Systems: A Comparative Study
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study
A. Cortiella
K. Park
Alireza Doostan
19
15
0
29 Jan 2022
Discovery of interpretable structural model errors by combining Bayesian
  sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test
  case
Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test case
R. Mojgani
Ashesh Chattopadhyay
Pedram Hassanzadeh
27
15
0
01 Oct 2021
DySMHO: Data-Driven Discovery of Governing Equations for Dynamical
  Systems via Moving Horizon Optimization
DySMHO: Data-Driven Discovery of Governing Equations for Dynamical Systems via Moving Horizon Optimization
F. Lejarza
M. Baldea
AI4CE
27
38
0
30 Jul 2021
Time-lagged autoencoders: Deep learning of slow collective variables for
  molecular kinetics
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
C. Wehmeyer
Frank Noé
AI4CE
BDL
111
356
0
30 Oct 2017
1