Dresden 2020 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 58: Poster: Nonlinear Dynamics; Pattern Formation; Networks; Delay Systems; Synchronization

DY 58.12: Poster

Thursday, March 19, 2020, 15:00–18:00, P1C

Data-driven identification of sparsely observed stochastic systems — •Dimitra Maoutsa and Manfred Opper — Technical University of Berlin, Berlin, Germany

Stochastic differential equations naturally occur in many fields in science and engineering, arising often as descriptions of systems with unresolved fast degrees of freedom. Usually, the underlying deterministic dynamics (i.e. drift function) and complete path trajectories of such systems are unknown, but instead we only have discrete time state observations at hand. Existing inference methods for such systems, either consider detailed parametric drift models, or assume densely observed trajectories for non-parametric Gaussian process drift estimation. Here, we present a data-driven approach for recovering nonlinear stochastic systems from sparse state observations. By introducing a novel method for simulating stochastic bridges consistent with the observed time series state space structure, we effectively create dense sample paths required for non-parametric drift estimation. We illustrate the power of our method on a number of simulated canonical dynamical systems, demonstrating thereby its potential and accuracy compared to existing frameworks.