Publications

Matthew Millard, Norman Stutzig, Jörg Fehr, and Tobias Siebert. A benchmark of muscle models to length changes great and small. ScienceDirect Elsevier, September 2024. [PUMA: lengthening Active Force-length Force-velocity relation Benchmark LS-DYNA Muscle model Impedance] URL

Joachim Escher, Pierre Gosselet, and Christina Lienstromberg. A note on model reduction for microelectromechanical systems. Nonlinearity, (30)2:454--465, 2017. [PUMA: Lienstromberg IADM systems microelectromechanical for model reduction] URL

Philipp Schenk, Stefan Papenkort, Markus Böl, Tobias Siebert, Roland Grassme, and Christian Rode. A simple geometrical model accounting for 3D muscle architectural changes across muscle lengths. In Tobias Siebert (Eds.), Journal of Biomechanics, (103):109694, Elsevier BV, April 2020. [PUMA: Pennation angle Rabbit length Muscle model Fascicle architecture soleus] URL

Architectural model for muscle growth during maturation.. In Tobias Siebert (Eds.), Biomechanics and Modeling in Mechanobiology, 20:2031–2044, July 2021. [PUMA: morphology Aponeurosis Pennation angle length Muscle model Fascicle architecture] URL

Stefan Papenkort, Markus Boel, and Tobias Siebert. Architectural model for muscle growth during maturation. In Tobias Siebert (Eds.), Biomechanics and Modeling in Mechanobiology, (20)5:2031--2044, Oct 1, 2021. [PUMA: morphology Papenkort length Fascicle Inspo Pennation Aponeurosis angle Muscle model Siebert architecture] URL

Matthias Lorenzen, Fabrizio Dabbene, Roberto Tempo, and Frank Allgöwer. Constraint-Tightening and Stability in Stochastic Model Predictive Control. IEEE Trans. Automat. Control, (62)7:3165-3177, 2017. [PUMA: control;chance stability;Numerical processes;Uncertainty;Stochastic control;randomized Asymptotic stability;Optimization;Predictive predictive control horizon constraints;constrained control;discrete-time stochastic control;Robustness;Stochastic model systems;predictive algorithms;receding]

Matthias Lorenzen, Matthias A. Müller, and Frank Allgöwer. Stochastic Model Predictive Control without Terminal Constraints. Int. J. Robust and Nonlinear Control, 2017. [PUMA: without predictive control terminal constraints, constrained systems, nonlinear stochastic control, model MPC]

Florian A. Bayer, Matthias A. Müller, and Frank Allgöwer. On optimal system operation in robust economic MPC. Automatica, (88):98 - 106, 2018. [PUMA: Economic disturbances Robust predictive Stochastic control, model] URL

Philipp N. Köhler, Matthias A. Müller, and Frank Allgöwer. A distributed economic MPC framework for cooperative control under conflicting objectives. Automatica, (96):368 - 379, 2018. [PUMA: Economic systems Distributed Collaborative predictive control, model] URL

Magnus Redeker, and Bernard Haasdonk. A POD-EIM reduced two-scale model for crystal growth. Advances in Computational Mathematics, (41)5:987--1013, Springer US, 2015. [PUMA: reduction; Empirical two-scale decomposition; Parametrized 78M34 Proper orthogonal interpolation; Model model; vorlaeufig] URL

Daniel Wirtz, and Bernard Haasdonk. A-posteriori error estimation for parameterized kernel-based systems. Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling, 2012. [PUMA: subspace error dynamical kernel a-posteriori methods, systems, nonlinear offline/online decomposition, parameterized projection estimates, model vorlaeufig reduction,] URL

Andrea Barth, Raimund B�rger, Ilja Kröker, and Christian Rohde. Computational uncertainty quantification for a clarifier-thickener model with several random perturbations: A hybrid stochastic Galerkin approach. Computers & Chemical Engineering, (89):11 -- 26, 2016. [PUMA: Clarifier-thickener model vorlaeufig] URL

D. Wirtz, and B. Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems and Control Letters, (61)1:203 - 211, 2012. [PUMA: subspace error dynamical kernel a-posteriori methods, systems, nonlinear offline/online decomposition, projection estimates, model vorlaeufig reduction,] URL

Florian A. Bayer, Matthias Lorenzen, Matthias A. Mueller, and Frank Allgoewer. Robust economic Model Predictive Control using stochastic information. AUTOMATICA, (74):151-161, PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND, December 2016. [PUMA: disturbances} Robust {Economic control; predictive Stochastic model]

Christian Burmeister, Dirk Luettgens, and Frank T. Piller. Business Model Innovation for Industrie 4.0: Why the 'Industrial Internet' Mandates a New Perspective on Innovation. SSRN eLibrary, SSRN, 2015. [PUMA: Business Model industrie4.0]