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

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

Mattia Cenedese
Joar Axås
Bastian Bäuerlein
Kerstin Avila
George Haller
ArXivPDFHTML

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

5 / 5 papers shown
Title
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
91
1
0
13 Jul 2024
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
49
5
0
16 Oct 2023
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
107
39
0
05 Oct 2021
Deep learning for universal linear embeddings of nonlinear dynamics
Deep learning for universal linear embeddings of nonlinear dynamics
Bethany Lusch
J. Nathan Kutz
Steven L. Brunton
57
1,237
0
27 Dec 2017
Linearly-Recurrent Autoencoder Networks for Learning Dynamics
Linearly-Recurrent Autoencoder Networks for Learning Dynamics
Samuel E. Otto
C. Rowley
AI4CE
34
322
0
04 Dec 2017

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.