Berlin 2024 – wissenschaftliches Programm

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DY: Fachverband Dynamik und Statistische Physik

DY 42: Focus Session: Computing with Dynamical Systems: New Perspectives on Reservoirs and Applications I – Fundamentals

Donnerstag, 21. März 2024, 09:30–12:15, BH-N 243

Reservoir Computing uses the dynamic response of driven dynamical systems to predict and analyze temporal signals. The best-known example is the Echo State Networks proposed by H. Jaeger in 2001, where the reservoir consists of a recurrent neural network. However, in recent years, other realizations have been proposed, in particular those in which the reservoir system can be implemented in hardware in a fast and energy-efficient manner (e.g., using optical components). Other recent developments concern the specific incorporation of prior (physical) knowledge about the source of the input signal (physics aware/informed Reservoir Computing) as well as various application examples in different disciplines. Therefore, the goal of this focus session is to highlight the rapidly advancing current developments in the field of Reservoir Computing in order to enable a direct scientific exchange between new methodological approaches and innovative applications.

Organized by Ulrich Parlitz (Göttingen), Kathy Lüdge (Ilmenau), and Christoph Räth (München)

	09:30	DY 42.1	Hauptvortrag: Is predicting chaos and extreme dynamics possible? An overview of (some) scientific machine learning approaches — •Luca Magri
	10:00	DY 42.2	Harnessing multistability: Expanding the capabilities of reservoir computers via multifunctionality — •Andrew Flynn, Vassilios Tsachouridis, and Andreas Amann
	10:15	DY 42.3	Generation of persistent memory using stable chaos in random neural networks — •Hiromichi Suetani
	10:30	DY 42.4	Enhancing reservoir predictions of chaotic time series by incorporating delayed values of input and reservoir variables — •Luk Fleddermann and Ulrich Parlitz
	10:45		15 min. break
	11:00	DY 42.5	Prediction of spatio-temporal chaos using parallel reservoir computing in combination with dimensionality reduction — Kai-Uwe Hollborn, Luk Fleddermann, •Gerrit Wellecke, and Ulrich Parlitz
	11:15	DY 42.6	High Dimensional Hybrid Reservoir Computing — •Tamon Nakano, Sebastian Baur, and Christoph Räth
	11:30	DY 42.7	Exploiting the Brownian motion of quasi-particles for unconventional computing — •Alessandro Pignedoli, Björn Dörschel, and Karin Everschor-Sitte
	11:45	DY 42.8	Designing Active Matter Systems for Reservoir Computing — •Mario U. Gaimann and Miriam Klopotek
	12:00	DY 42.9	Contextual Alignment for Robust Learning in Dynamical Systems — •Max Weinmann and Miriam Klopotek