Deep learning for structure-preserving universal stable Koopman-inspired embeddings for nonlinear canonical Hamiltonian dynamics

• DOI: 10.1088/2632-2153/adb9b5
• Publication Date: 2025-02-24T22:52:58Z
• Authors: Pawan Goyal; Süleyman Yıldız; Peter Benner
• Keywords: FOS: Computer and information sciences; Computer engineering. Computer hardware; Computer Science - Machine Learning; symplectic transformation; Computer Science - Artificial Intelligence; linear systems; QA75.5-76.95; Dynamical Systems (math.DS); lifting-principle; Machine Learning (cs.LG); TK7885-7895; Artificial Intelligence (cs.AI); canonical Hamiltonian systems; Electronic computers. Computer science; FOS: Mathematics; nonlinear systems; Mathematics - Dynamical Systems; Koopman operator

Abstract Discovering a suitable coordinate transformation for nonlinear systems enables the construction of simpler models, facilitating prediction, control, and optimization for complex nonlinear systems. To that end, Koopman operator theory offers a framework for global linearization of nonlinear systems, thereby allowing the usage of linear tools for design studies. In this work, we focus on the identification of global linearized embeddings for canonical nonlinear Hamiltonian systems through a symplectic transformation. While this task is often challenging, we leverage the power of deep learning to discover the desired embeddings. Furthermore, to overcome the shortcomings of Koopman operators for systems with continuous spectra, we apply the lifting principle and learn global cubicized embeddings. Additionally, a key emphasis is given to enforce the bounded stability for the dynamics of the discovered embeddings. We demonstrate the capabilities of deep learning in acquiring compact symplectic coordinate transformations and the corresponding simple dynamical models, fostering data-driven learning of nonlinear canonical Hamiltonian systems, even those with continuous spectra.