Extensions of the Path-integral formula for computation of Koopman eigenfunctions
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866915033551732736 |
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| author | Deka, Shankar A. Vaidya, Umesh |
| author_facet | Deka, Shankar A. Vaidya, Umesh |
| contents | Representing nonlinear dynamical systems using the Koopman Operator and its spectrum has distinct advantages in terms of linear interpretability of the model as well as in analysis and control synthesis through the use of well-studied techniques from linear systems theory. As such, efficient computation of Koopman eigenfunctions is of paramount importance towards enabling such Koopman-based constructions. To this end, several approaches have been proposed in literature, including data-driven, convex optimization, and Deep Learning-based methods. In our recent work, we proposed a novel approach based on path-integrals that allowed eigenfunction computations using a closed-form formula. In this paper, we present several important developments such as finite-time computations, relaxation of assumptions on the distribution of the principal Koopman eigenvalues, as well as extension towards saddle point systems, which greatly enhance the practical applicability of our method. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_16605 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Extensions of the Path-integral formula for computation of Koopman eigenfunctions Deka, Shankar A. Vaidya, Umesh Systems and Control Dynamical Systems Representing nonlinear dynamical systems using the Koopman Operator and its spectrum has distinct advantages in terms of linear interpretability of the model as well as in analysis and control synthesis through the use of well-studied techniques from linear systems theory. As such, efficient computation of Koopman eigenfunctions is of paramount importance towards enabling such Koopman-based constructions. To this end, several approaches have been proposed in literature, including data-driven, convex optimization, and Deep Learning-based methods. In our recent work, we proposed a novel approach based on path-integrals that allowed eigenfunction computations using a closed-form formula. In this paper, we present several important developments such as finite-time computations, relaxation of assumptions on the distribution of the principal Koopman eigenvalues, as well as extension towards saddle point systems, which greatly enhance the practical applicability of our method. |
| title | Extensions of the Path-integral formula for computation of Koopman eigenfunctions |
| topic | Systems and Control Dynamical Systems |
| url | https://arxiv.org/abs/2411.16605 |