146 citations · 222 across the 32 of their papers we have counts for
36 papers
Structure-Preserving Data-Driven Identification of Port-Hamiltonian Differential-Algebraic Systems
Tom Zwerschke, Matthias Ehrhardt, Michael Günther +1
We present a data-driven approach to identifying linear index-1 differential-algebraic pH systems (pH-DAEs) based on input-output measurements. In comparison to the identification…
Feedback approaches for set-point stabilization of interacting particle systems
Daniel Jannik Happ, Birgit Jacob, Claudia Totzeck
We explore the problem of asymptotically stabilizing a class of interacting particle systems to a prescribed particle configuration with zero velocity. The class of interacting par…
A Consensus-based optimization algorithm using Gaussian processes for global optimization problems in Sobolev spaces
Mahmoud Khatab, Claudia Totzeck
We propose an algorithm to approximate solutions of global optimization problems in Sobolev spaces that follows the spirit of Consensus-based algorithms in finite dimensions. The m…
Analysis and Simulation of Cooperative Buffer Strategies for Traffic Wave Mitigation
Raphael Korbmacher, Daniel Straub, Antoine Tordeux +1
Stop-and-go waves in traffic flow pose a persistent challenge, compromising safety, efficiency, and environmental sustainability. This paper presents a novel mitigation strategy id…
A Space Mapping approach for the calibration of financial models with the application to the Heston model
Anna Clevenhaus, Claudia Totzeck, Matthias Ehrhardt
We present a novel approach for parameter calibration of the Heston model for pricing an Asian put option, namely space mapping. Since few parameters of the Heston model can be dir…
Localized KBO with genetic dynamics for multi-modal optimization
Federica Ferrarese, Claudia Totzeck
In this paper, we introduce a novel approach to multi-modal optimization by enhancing the recently developed kinetic-based optimization (KBO) method with genetic dynamics (GKBO). T…
CBX: Python and Julia packages for consensus-based interacting particle methods
Rafael Bailo, Alethea Barbaro, Susana N. Gomes +4
We introduce CBXPy and ConsensusBasedX.jl, Python and Julia implementations of consensus-based interacting particle systems (CBX), which generalise consensus-based optimization met…
Time-continuous microscopic pedestrian models: an overview
Raphael Korbmacher, Alexandre Nicolas, Antoine Tordeux +1
We give an overview of time-continuous pedestrian models with a focus on data-driven modelling. Starting from pioneer, reactive force-based models we move forward to modern, active…
Kinetic based optimization enhanced by genetic dynamics
Giacomo Albi, Federica Ferrarese, Claudia Totzeck
We propose and analyse a variant of the recently introduced kinetic based optimization method that incorporates ideas like survival-of-the-fittest and mutation strategies well-know…
Modeling Minimum Cost Network Flows With Port-Hamiltonian Systems
Onur Tanil Doganay, Kathrin Klamroth, Bruno Lang +2
We give a short overview of advantages and drawbacks of the classical formulation of minimum cost network flow problems and solution techniques, to motivate a reformulation of clas…
A new perspective on dynamic network flow problems via port-Hamiltonian systems
Onur Tanil Doganay, Kathrin Klamroth, Bruno Lang +2
We suggest a global perspective on dynamic network flow problems that takes advantage of the similarities to port-Hamiltonian dynamics. Dynamic minimum cost flow problems are formu…
Asymptotic Analysis for Optimal Control of the Cattaneo Model
Sebastian Blauth, René Pinnau, Matthias Andres +1
We consider an optimal control problem with tracking-type cost functional constrained by the Cattaneo equation, which is a well-known model for delayed heat transfer. In particular…
Structure-preserving identification of port-Hamiltonian systems -- a sensitivity-based approach
Michael Günther, Birgit Jacob, Claudia Totzeck
We present a gradient-based calibration algorithm to identify a port-Hamiltonian system from given time-domain input-output data. The gradient is computed with the help of sensitiv…
Erratum and original of Port-Hamiltonian structure of interacting particle systems and its mean-field limit
Jannik Daun, Daniel Jannik Happ, Birgit Jacob +1
We derive a minimal port-Hamiltonian formulation of a general class of interacting particle systems driven by alignment and potential-based force dynamics which include the Cucker-…
Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain
Michael Günther, Birgit Jacob, Claudia Totzeck
We present a gradient-based identification algorithm to identify the system matrices of a linear port-Hamiltonian system from given input-output time data. Aiming for a direct stru…
Consensus-Based Optimization for Multi-Objective Problems: A Multi-Swarm Approach
Kathrin Klamroth, Michael Stiglmayr, Claudia Totzeck
We propose a multi-swarm approach to approximate the Pareto front of general multi-objective optimization problems that is based on the Consensus-based Optimization method (CBO). T…
Multi-scale description of pedestrian collective dynamics with port-Hamiltonian systems
Antoine Tordeux, Claudia Totzeck
Port-Hamiltonian systems (PHS) theory is a recent but already well-established modelling approach for non-linear physical systems. Some studies have shown lately that PHS framework…
Ensemble-based gradient inference for particle methods in optimization and sampling
Claudia Schillings, Claudia Totzeck, Philipp Wacker
We propose an approach based on function evaluations and Bayesian inference to extract higher-order differential information of objective functions {from a given ensemble of partic…
Gradient-based parameter calibration of an anisotropic interaction model for pedestrian dynamics
Zhomart Turarov, Claudia Totzeck
We propose an extension of the anisotropic interaction model which allows for collision avoidance in pairwise interactions by a rotation of forces \cite{arXiv:1912.04234} by includ…
Consensus-based Optimization and Ensemble Kalman Inversion for Global Optimization Problems with Constraints
J. A. Carrillo, C. Totzeck, U. Vaes
We introduce a practical method for incorporating equality and inequality constraints in global optimization methods based on stochastic interacting particle systems, specifically…
A gradient based calibration method for the Heston model
Anna Clevenhaus, Claudia Totzeck, Matthias Ehrhardt
The Heston model is a well-known two-dimensional financial model. Because the Heston model contains implicit parameters that cannot be determined directly from real market data, ca…
Parameter calibration with Consensus-based Optimization for interaction dynamics driven by neural networks
Simone Göttlich, Claudia Totzeck
We calibrate parameters of neural networks that model forces in interaction dynamics with the help of the Consensus-based global optimization method (CBO). We state the general fra…
Trends in Consensus-based optimization
Claudia Totzeck
In this chapter we give an overview of the consensus-based global optimization algorithm and its recent variants. We recall the formulation and analytical results of the original m…
Space mapping-based optimization with the macroscopic limit of interacting particle systems
Jennifer Weißen, Simone Göttlich, Claudia Totzeck
We propose a space mapping-based optimization algorithm for microscopic interacting particle dynamics which are inappropriate for direct optimization. This is of relevance for exam…