Výsledky vyhledávání - "Lehmberg, Daniel"

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Data-driven modelling of brain activity using neural networks, Diffusion Maps, and the Koopman operator

Autor: Gallos, Ioannis K., Lehmberg, Daniel, Dietrich, Felix, Siettos, Constantinos

Publikováno v: Chaos 34, 013151 (2024)

We propose a machine-learning approach to model long-term out-of-sample dynamics of brain activity from task-dependent fMRI data. Our approach is a three stage one. First, we exploit Diffusion maps (DMs) to discover a set of variables that parametriz

Externí odkaz: http://arxiv.org/abs/2304.11925

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Double Diffusion Maps and their Latent Harmonics for Scientific Computations in Latent Space

Autor: Evangelou, Nikolaos, Dietrich, Felix, Chiavazzo, Eliodoro, Lehmberg, Daniel, Meila, Marina, Kevrekidis, Ioannis G.

We introduce a data-driven approach to building reduced dynamical models through manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold learning technique) on time series data. A second round of Diffusion Maps on t

Externí odkaz: http://arxiv.org/abs/2204.12536

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Akademický článek

Double Diffusion Maps and their Latent Harmonics for scientific computations in latent space

Autor: Evangelou, Nikolaos, Dietrich, Felix, Chiavazzo, Eliodoro, Lehmberg, Daniel, Meila, Marina, Kevrekidis, Ioannis G.

Publikováno v: In Journal of Computational Physics 15 July 2023 485

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Can we learn where people go?

Autor: Gödel, Marion, Köster, Gerta, Lehmberg, Daniel, Gruber, Manfred, Kneidl, Angelika, Sesser, Florian

In most agent-based simulators, pedestrians navigate from origins to destinations. Consequently, destinations are essential input parameters to the simulation. While many other relevant parameters as positions, speeds and densities can be obtained fr

Externí odkaz: http://arxiv.org/abs/1812.03719

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