Active search for bifurcations
Name
053159_1_5.0226625.pdf
Description
Published version
Size
1.97 MB
Format
Adobe PDF
Checksum (MD5)
51760b80397d96aa21c4f39320374a3f
Author(s)
Psarellis, Yorgos M
Sapsis, Themistoklis P
Kevrekidis, Ioannis G
Date Issued
May 28, 2025
Journal
Chaos: An Interdisciplinary Journal of Nonlinear Science
Publisher
AIP Publishing
Citation
Yorgos M. Psarellis, Themistoklis P. Sapsis, Ioannis G. Kevrekidis; Active search for bifurcations. Chaos 1 May 2025; 35 (5): 053159.
Version
Final published version
Abstract
Bifurcations mark qualitative changes of long-term behavior in dynamical systems and can often signal sudden (“hard”) transitions or catastrophic events (divergences). Accurately locating them is critical not just for deeper understanding of observed dynamic behavior, but also for designing efficient interventions. When the dynamical system at hand is complex, possibly noisy, and expensive to sample, standard (e.g., continuation based) numerical methods may become impractical. We propose an active learning framework, where Bayesian Optimization is leveraged to discover saddle-node or Hopf bifurcations, from a judiciously chosen small number of vector field observations. Such an approach becomes especially attractive in systems whose state × parameter space exploration is resource-limited. It also naturally provides a framework for uncertainty quantification (aleatoric and epistemic), useful in systems with inherent stochasticity.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1063/5.0226625
