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Markov Neural Operators for Learning Chaotic Systems

Li, Zongyi and Kovachki, Nikola and Azizzadenesheli, Kamyar and Liu, Burigede and Bhattacharya, Kaushik and Stuart, Andrew and Anandkumar, Anima (2021) Markov Neural Operators for Learning Chaotic Systems. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210719-210135878

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Abstract

Chaotic systems are notoriously challenging to predict because of their instability. Small errors accumulate in the simulation of each time step, resulting in completely different trajectories. However, the trajectories of many prominent chaotic systems live in a low-dimensional subspace (attractor). If the system is Markovian, the attractor is uniquely determined by the Markov operator that maps the evolution of infinitesimal time steps. This makes it possible to predict the behavior of the chaotic system by learning the Markov operator even if we cannot predict the exact trajectory. Recently, a new framework for learning resolution-invariant solution operators for PDEs was proposed, known as neural operators. In this work, we train a Markov neural operator (MNO) with only the local one-step evolution information. We then compose the learned operator to obtain the global attractor and invariant measure. Such a Markov neural operator forms a discrete semigroup and we empirically observe that does not collapse or blow up. Experiments show neural operators are more accurate and stable compared to previous methods on chaotic systems such as the Kuramoto-Sivashinsky and Navier-Stokes equations.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2106.06898arXivDiscussion Paper
ORCID:
AuthorORCID
Li, Zongyi0000-0003-2081-9665
Kovachki, Nikola0000-0002-3650-2972
Azizzadenesheli, Kamyar0000-0001-8507-1868
Liu, Burigede0000-0002-6518-3368
Bhattacharya, Kaushik0000-0003-2908-5469
Additional Information:Z. Li gratefully acknowledges the financial support from the Kortschak Scholars Program. A. Anandkumar is supported in part by Bren endowed chair, LwLL grants, Beyond Limits, Raytheon, Microsoft, Google, Adobe faculty fellowships, and DE Logi grant. K. Bhattacharya, N. B. Kovachki, B. Liu, and A. M. Stuart gratefully acknowledge the financial support of the Army Research Laboratory through the Cooperative Agreement Number W911NF-12-0022. Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-12-2-0022. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
Funders:
Funding AgencyGrant Number
Kortschak Scholars ProgramUNSPECIFIED
Bren Professor of Computing and Mathematical SciencesUNSPECIFIED
Learning with Less Labels (LwLL)UNSPECIFIED
Beyond LimitsUNSPECIFIED
Raytheon CompanyUNSPECIFIED
Microsoft Faculty FellowshipUNSPECIFIED
Google Faculty Research AwardUNSPECIFIED
AdobeUNSPECIFIED
Caltech De Logi FundUNSPECIFIED
Army Research LaboratoryW911NF-12-0022
Army Research LaboratoryW911NF-12-2-0022
Record Number:CaltechAUTHORS:20210719-210135878
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210719-210135878
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109918
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:19 Jul 2021 22:10
Last Modified:19 Jul 2021 22:10

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