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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

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

26 / 26 papers shown

Title
Data-driven discovery of mechanical models directly from MRI spectral data D. G. J. Heesterbeek M. H. C. van Riel T. van Leeuwen C. A. T. van den Berg A. Sbrizzi MedIm 107 0 0 11 Nov 2024
Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction Manuel Brenner Christoph Jürgen Hemmer Zahra Monfared Daniel Durstewitz AI4CE 80 4 0 18 Oct 2024
On latent dynamics learning in nonlinear reduced order modeling N. Farenga S. Fresca Simone Brivio Andrea Manzoni AI4CE 67 1 0 27 Aug 2024
Out-of-Domain Generalization in Dynamical Systems Reconstruction Niclas Alexander Göring Florian Hess Manuel Brenner Zahra Monfared Daniel Durstewitz AI4CE 102 16 0 28 Feb 2024
Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data Lloyd Fung Urban Fasel M. Juniper 55 2 0 23 Feb 2024
Sparse discovery of differential equations based on multi-fidelity Gaussian process Yuhuang Meng Yue Qiu 141 0 0 22 Jan 2024
$Improved identification accuracy in equation learning via comprehensive $\boldsymbol{R^2}$-elimination and Bayesian model selection$ Improved identification accuracy in equation learning via comprehensive $\boldsymbol{R^2}$ -elimination and Bayesian model selection Daniel Nickelsen B. Bah 118 0 0 22 Nov 2023
Interpretable structural model error discovery from sparse assimilation increments using spectral bias-reduced neural networks: A quasi-geostrophic turbulence test case R. Mojgani Ashesh Chattopadhyay Pedram Hassanzadeh 67 9 0 22 Sep 2023
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes Junette Hsin Shubhankar Agarwal Adam J. Thorpe Luis Sentis David Fridovich-Keil 129 2 0 20 Sep 2023
A Robust SINDy Approach by Combining Neural Networks and an Integral Form Ali Forootani P. Goyal P. Benner 93 4 0 13 Sep 2023
Learning noise-induced transitions by multi-scaling reservoir computing Zequn Lin Zhaofan Lu Zengru Di Ying Tang 92 4 0 11 Sep 2023
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 108 77 0 21 May 2023
Multiphysics discovery with moving boundaries using Ensemble SINDy and Peridynamic Differential Operator A. Bekar E. Haghighat E. Madenci AI4CE 59 2 0 27 Mar 2023
Benchmarking sparse system identification with low-dimensional chaos A. Kaptanoglu Lanyue Zhang Zachary G. Nicolaou Urban Fasel Steven L. Brunton 98 24 0 04 Feb 2023
Investigating Sindy As a Tool For Causal Discovery In Time Series Signals Andrew O'Brien Rosina O. Weber Edward J. Kim CML 63 6 0 29 Dec 2022
Asymptotic consistency of the WSINDy algorithm in the limit of continuum data Daniel Messenger David M. Bortz 139 13 0 29 Nov 2022
Noise-aware Physics-informed Machine Learning for Robust PDE Discovery Pongpisit Thanasutives Takeshi Morita M. Numao Ken-ichi Fukui PINN AI4CE 119 19 0 26 Jun 2022
Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning D. Bistrian Omer San Ionel M. Navon 48 1 0 17 Jun 2022
Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects Megan R. Ebers K. Steele J. Nathan Kutz 85 16 0 10 Mar 2022
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study A. Cortiella K. Park Alireza Doostan 73 16 0 29 Jan 2022
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 87 16 0 01 Oct 2021
DySMHO: Data-Driven Discovery of Governing Equations for Dynamical Systems via Moving Horizon Optimization F. Lejarza M. Baldea AI4CE 79 39 0 30 Jul 2021
Discovering Sparse Interpretable Dynamics from Partial Observations Peter Y. Lu Joan Ariño Bernad Marin Soljacic AI4CE 83 25 0 22 Jul 2021
A Framework for Machine Learning of Model Error in Dynamical Systems Matthew E. Levine Andrew M. Stuart 111 69 0 14 Jul 2021
Uncovering Closed-form Governing Equations of Nonlinear Dynamics from Videos Lele Luan Yang Liu Hao Sun PINN AI4CE 43 0 0 09 Jun 2021
Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation Georg Gottwald Sebastian Reich AI4CE 69 62 0 14 Jul 2020