5 papers
Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offs
Pongpisit Thanasutives, Naichang Ke, Yoshinobu Kawahara
We propose KO-PDE-IDENT, a data-driven framework for identifying parsimonious partial differential equations (PDEs) with false discovery rate (FDR) control. PDE discovery from nois…
Koopman operators with intrinsic observables in rigged reproducing kernel Hilbert spaces
Isao Ishikawa, Yuka Hashimoto, Masahiro Ikeda +1
This paper presents a novel approach for estimating the Koopman operator defined on a reproducing kernel Hilbert space (RKHS) and its spectra. We propose an estimation method, what…
Generalized Stochastic Resilience for Early Warning Signals Based on Koopman Operator
Yuta Miyauchi, Masahiro Ikeda, Yoshinobu Kawahara
Developing methods for detecting tipping phenomena at an early stage is an important problem in various fields such as ecology, medicine, and economics. A tipping phenomenon is cha…
Operator-theoretic Analysis of Mutual Interactions in Synchronized Dynamics
Yuka Hashimoto, Masahiro Ikeda, Hiroya Nakao +1
Analyzing synchronized nonlinear oscillators is one of the most important and attractive topics in nonlinear science. By understanding the interactions between the oscillators, we…
Koopman Spectrum Nonlinear Regulators and Efficient Online Learning
Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey +3
Most modern reinforcement learning algorithms optimize a cumulative single-step cost along a trajectory. The optimized motions are often 'unnatural', representing, for example, beh…