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Data-Driven Modeling and Prediction of Non-Linearizable Dynamics via
  Spectral Submanifolds

Data-Driven Modeling and Prediction of Non-Linearizable Dynamics via Spectral Submanifolds

Nature Communications (Nat Commun), 2022
13 January 2022
Mattia Cenedese
Joar Axås
Bastian Bäuerlein
Kerstin Avila
George Haller
ArXiv (abs)PDFHTML

Papers citing "Data-Driven Modeling and Prediction of Non-Linearizable Dynamics via Spectral Submanifolds"

22 / 22 papers shown
RNNs perform task computations by dynamically warping neural representations
RNNs perform task computations by dynamically warping neural representations
Arthur Pellegrino
Angus Chadwick
148
3
0
03 Dec 2025
DyMixOp: A Neural Operator Designed from a Complex Dynamics Perspective with Local-Global Mixing for Solving PDEs
DyMixOp: A Neural Operator Designed from a Complex Dynamics Perspective with Local-Global Mixing for Solving PDEs
Pengyu Lai
Yixiao Chen
Hui Xu
Rui Wang
Feng Wang
Hui Xu
AI4CE
218
0
0
19 Aug 2025
Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics
Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics
Alex Guo
Michael D. Graham
AI4CE
249
0
0
21 Jul 2025
A discrete physics-informed training for projection-based reduced order models with neural networks
A discrete physics-informed training for projection-based reduced order models with neural networks
N. Sibuet
S. A. D. Parga
J. R. Bravo
R. Rossi
380
4
0
31 Mar 2025
Hybrid Time-Domain Behavior Model Based on Neural Differential Equations and RNNs
Hybrid Time-Domain Behavior Model Based on Neural Differential Equations and RNNs
Zenghui Chang
Yang Zhang
Hu Tan
Hong Cai Chen
241
0
0
28 Mar 2025
Data-Driven Soft Robot Control via Adiabatic Spectral Submanifolds
Data-Driven Soft Robot Control via Adiabatic Spectral Submanifolds
Roshan S. Kaundinya
J. I. Alora
Jonas G. Matt
Luis A. Pabon
Marco Pavone
George Haller
212
3
0
13 Mar 2025
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictionsChaos (Chaos), 2024
Jake Buzhardt
C. Ricardo Constante-Amores
Michael D. Graham
449
7
0
20 Nov 2024
Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine
Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine
Ionut-Gabriel Farcas
Rayomand P. Gundevia
R. Munipalli
Karen E. Willcox
AI4CE
329
10
0
13 Jul 2024
Machine-learning invariant foliations in forced systems for reduced
  order modelling
Machine-learning invariant foliations in forced systems for reduced order modelling
R. Szalai
216
1
0
21 Mar 2024
Information theory for dimensionality reduction in dynamical systems
Information theory for dimensionality reduction in dynamical systems
Matthew S. Schmitt
Maciej Koch-Janusz
Michel Fruchart
Daniel S. Seara
Michael Rust
Vincenzo Vitelli
319
7
0
11 Dec 2023
Generative learning for nonlinear dynamics
Generative learning for nonlinear dynamics
William Gilpin
AI4CEPINN
379
53
0
07 Nov 2023
Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder
Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder
Priyam Gupta
Peter J. Schmid
D. Sipp
T. Sayadi
Georgios Rigas
436
20
0
16 Oct 2023
Robust Nonlinear Reduced-Order Model Predictive Control
Robust Nonlinear Reduced-Order Model Predictive ControlIEEE Conference on Decision and Control (CDC), 2023
J. I. Alora
Luis A. Pabon
Johannes Köhler
Mattia Cenedese
Edward Schmerling
Melanie Zeilinger
George Haller
Marco Pavone
148
9
0
11 Sep 2023
Learning Nonlinear Projections for Reduced-Order Modeling of Dynamical
  Systems using Constrained Autoencoders
Learning Nonlinear Projections for Reduced-Order Modeling of Dynamical Systems using Constrained AutoencodersChaos (Chaos), 2023
Samuel E. Otto
G. Macchio
C. Rowley
AI4CE
208
29
0
28 Jul 2023
Discovering Causal Relations and Equations from Data
Discovering Causal Relations and Equations from DataPhysics reports (Phys. Rep.), 2023
Gustau Camps-Valls
Andreas Gerhardus
Urmi Ninad
Gherardo Varando
Georg Martius
E. Balaguer-Ballester
Ricardo Vinuesa
Emiliano Díaz
L. Zanna
Jakob Runge
PINNAI4ClAI4CECML
291
125
0
21 May 2023
Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of
  spatio-temporal processes
Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes
Francesco Regazzoni
S. Pagani
M. Salvador
Luca Dede'
A. Quarteroni
AI4CE
258
8
0
28 Apr 2023
Benchmarking sparse system identification with low-dimensional chaos
Benchmarking sparse system identification with low-dimensional chaosNonlinear dynamics (Nonlinear Dyn.), 2023
A. Kaptanoglu
Lanyue Zhang
Zachary G. Nicolaou
Urban Fasel
Steven L. Brunton
284
45
0
04 Feb 2023
Changes from Classical Statistics to Modern Statistics and Data Science
Changes from Classical Statistics to Modern Statistics and Data Science
Kai Zhang
Shan-Yu Liu
M. Xiong
359
1
0
30 Oct 2022
Data-Driven Spectral Submanifold Reduction for Nonlinear Optimal Control
  of High-Dimensional Robots
Data-Driven Spectral Submanifold Reduction for Nonlinear Optimal Control of High-Dimensional RobotsIEEE International Conference on Robotics and Automation (ICRA), 2022
J. I. Alora
Mattia Cenedese
Edward Schmerling
George Haller
Marco Pavone
462
37
0
13 Sep 2022
Data-driven reduced order models using invariant foliations, manifolds
  and autoencoders
Data-driven reduced order models using invariant foliations, manifolds and autoencodersJournal of nonlinear science (J. Nonlinear Sci.), 2022
R. Szalai
AI4CE
185
14
0
24 Jun 2022
Enhancing Computational Fluid Dynamics with Machine Learning
Enhancing Computational Fluid Dynamics with Machine Learning
Ricardo Vinuesa
Steven L. Brunton
AI4CE
523
529
0
05 Oct 2021
Data-driven Nonlinear Model Reduction to Spectral Submanifolds in
  Mechanical Systems
Data-driven Nonlinear Model Reduction to Spectral Submanifolds in Mechanical Systems
Mattia Cenedese
Joar Axås
Haocheng Yang
M. Eriten
George Haller
359
52
0
05 Oct 2021
1
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