267 citations · 888 across the 15 of their papers we have counts for
19 papers
LiveCoMS Roundtable: Discussing Markov state models after two decades of development v1.0
Daniel M. Zuckerman, Gregory R. Bowman, Amedeo Caflisch +14
Markov state models (MSMs) play a central role in the analysis of molecular dynamics (MD) simulations for biomolecules, and MSM methodologies have been under continual development…
Optimal Bias Potentials via Ergodic Optimal Control and Generator Learning
Lei Guo, Carsten Hartmann, Thomas Berger +1
We investigate the computation of optimal bias potentials for accelerating transitions between metastable states and for computation of equilibrium properties in molecular dynamics…
Tensor-based Approximation of Molecular Kinetics: Generator Learning, Reaction Coordinates and Incremental Updating
Minakshi Verma, Peter Benner, Feliks Nüske
We present an approach to analyze long-timescale kinetics of molecular dynamics simulations - meta-stable states and transition timescales - by a tensor-based approximation of the…
Consistent Projection of Langevin Dynamics: Preserving Thermodynamics and Kinetics in Coarse-Grained Models
Vahid Nateghi, Lara Neureither, Selma Moqvist +3
Coarse graining (CG) is an important task for efficient modeling and simulation of complex multi-scale systems, such as the conformational dynamics of biomolecules. This work prese…
Efficient Approximation of Molecular Kinetics using Random Fourier Features
Feliks Nüske, Stefan Klus
Slow kinetic processes of molecular systems can be analyzed by computing dominant eigenpairs of the Koopman operator or its generator. In this context, the Variational Approach to…
Funnel Control for Langevin Dynamics
Thomas Berger, Feliks Nüske
We study tracking control for stochastic differential equations of Langevin type and describe a new conceptual approach to the sampling problem for those systems. The objective is…
Koopman analysis of quantum systems
Stefan Klus, Feliks Nüske, Sebastian Peitz
Koopman operator theory has been successfully applied to problems from various research areas such as fluid dynamics, molecular dynamics, climate science, engineering, and biology.…
tgEDMD: Approximation of the Kolmogorov Operator in Tensor Train Format
Marvin Lücke, Feliks Nüske
Extracting information about dynamical systems from models learned off simulation data has become an increasingly important research topic in the natural and engineering sciences.…
Finite-data error bounds for Koopman-based prediction and control
Feliks Nüske, Sebastian Peitz, Friedrich Philipp +2
The Koopman operator has become an essential tool for data-driven approximation of dynamical (control) systems, e.g., via extended dynamic mode decomposition. Despite its popularit…
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
Stefan Klus, Patrick Gelß, Feliks Nüske +1
We derive symmetric and antisymmetric kernels by symmetrizing and antisymmetrizing conventional kernels and analyze their properties. In particular, we compute the feature space di…
Kernel-based approximation of the Koopman generator and Schrödinger operator
Stefan Klus, Feliks Nüske, Boumediene Hamzi
Many dimensionality and model reduction techniques rely on estimating dominant eigenfunctions of associated dynamical operators from data. Important examples include the Koopman op…
Tensor-based computation of metastable and coherent sets
Feliks Nüske, Patrick Gelß, Stefan Klus +1
Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory and related approaches. On the other hand, low-rank tensor p…
Coarse-graining Molecular Systems by Spectral Matching
Feliks Nüske, Lorenzo Boninsegna, Cecilia Clementi
Coarse-graining has become an area of tremendous importance within many different research fields. For molecular simulation, coarse-graining bears the promise of finding simplified…
Spectral Properties of Effective Dynamics from Conditional Expectations
Feliks Nüske, Péter Koltai, Lorenzo Boninsegna +1
The reduction of high-dimensional systems to effective models on a smaller set of variables is an essential task in many areas of science. For stochastic dynamics governed by diffu…
Sparse learning of stochastic dynamic equations
Lorenzo Boninsegna, Feliks Nüske, Cecilia Clementi
With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a frame…
Data-driven model reduction and transfer operator approximation
Stefan Klus, Feliks Nüske, Péter Koltai +4
In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to ap…
Markov State Models from short non-Equilibrium Simulations - Analysis and Correction of Estimation Bias
Feliks Nüske, Hao Wu, Jan-Hendrik Prinz +3
Many state of the art methods for the thermodynamic and kinetic characterization of large and complex biomolecular systems by simulation rely on ensemble approaches, where data fro…
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations
Hao Wu, Feliks Nüske, Fabian Paul +3
Markov state models (MSMs) and Master equation models are popular approaches to approximate molecular kinetics, equilibria, metastable states, and reaction coordinates in terms of…
A variational approach to modeling slow processes in stochastic dynamical systems
Frank Noé, Feliks Nüske
The slow processes of metastable stochastic dynamical systems are difficult to access by direct numerical simulation due the sampling problem. Here, we suggest an approach for mode…