Causal Local States: Achieving Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systems

Braun, Jonas (2026) Causal Local States: Achieving Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systems. Master's, Ludwig-Maximilians-Universität München.

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Item URL in elib:

https://elib.dlr.de/225693/

Document Type:

Thesis (Master's)

Title:

Causal Local States: Achieving Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systems

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Braun, Jonas	LMU	UNSPECIFIED	UNSPECIFIED

DLR Supervisors:

Contribution	DLR Supervisor	Institution or E-Mail	DLR Supervisor's ORCID iD
Thesis advisor	Räth, Christoph	Christoph.Raeth (at) dlr.de	UNSPECIFIED

Date:

2026

Open Access:

Yes

Number of Pages:

Status:

Published

Keywords:

time series analysis, AI, reservoir computing, causality measures, prediction, power grids

Institution:

Ludwig-Maximilians-Universität München

Department:

Fakultät für Physik

HGF - Research field:

other

HGF - Program:

other

HGF - Program Themes:

other

DLR - Research area:

Digitalisation

DLR - Program:

D KIZ - Artificial Intelligence

DLR - Research theme (Project):

D - short study [KIZ]

Location:

Köln-Porz

Institutes and Institutions:

Institute for Frontier Materials on Earth and in Space > Functional Granular and Composite Materials

Deposited By:

Räth, Christoph

Deposited On:

20 Jul 2026 10:55

Last Modified:

20 Jul 2026 10:55

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