Christof Schütte | DeepAI

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∙ 07/01/2023

Understanding recent deep-learning techniques for identifying collective variables of molecular dynamics

The dynamics of a high-dimensional metastable molecular system can often...

0 Wei Zhang, et al. ∙

∙ 05/04/2023

Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions

Numerical models are used widely for parameter reconstructions in the fi...

0 Matthias Plock, et al. ∙

∙ 12/13/2021

Data-driven modelling of nonlinear dynamics by polytope projections and memory

We present a numerical method to model dynamical systems from data. We u...

0 Niklas Wulkow, et al. ∙

∙ 01/07/2021

CINDy: Conditional gradient-based Identification of Non-linear Dynamics – Noise-robust recovery

Governing equations are essential to the study of nonlinear dynamics, of...

0 Alejandro Carderera, et al. ∙

∙ 12/14/2020

Data-driven model reduction of agent-based systems using the Koopman generator

The dynamical behavior of social systems can be described by agent-based...

0 Jan-Hendrik Niemann, et al. ∙

∙ 11/25/2020

Feature space approximation for kernel-based supervised learning

We propose a method for the approximation of high- or even infinite-dime...

0 Patrick Gelß, et al. ∙

∙ 04/02/2020

Kernel autocovariance operators of stationary processes: Estimation and convergence

We consider autocovariance operators of a stationary stochastic process ...

0 Mattes Mollenhauer, et al. ∙

∙ 09/23/2019

Data-driven approximation of the Koopman generator: Model reduction, system identification, and control

We derive a data-driven method for the approximation of the Koopman gene...

0 Stefan Klus, et al. ∙

∙ 04/18/2019

A kernel-based method for coarse graining complex dynamical systems

We present a novel kernel-based machine learning algorithm for identifyi...

0 Andreas Bittracher, et al. ∙

∙ 09/28/2018

A kernel-based approach to molecular conformation analysis

We present a novel machine learning approach to understanding conformati...

2 Stefan Klus, et al. ∙

∙ 07/24/2018

Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces

Reproducing kernel Hilbert spaces (RKHSs) play an important role in many...

0 Mattes Mollenhauer, et al. ∙

∙ 06/11/2015

Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

Background: High-throughput proteomics techniques, such as mass spectrom...

0 Tim Conrad, et al. ∙

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