@unpublished{21600,
abstract = {Many problems in science and engineering require the efficient numerical
approximation of integrals, a particularly important application being the
numerical solution of initial value problems for differential equations. For
complex systems, an equidistant discretization is often inadvisable, as it
either results in prohibitively large errors or computational effort. To this
end, adaptive schemes have been developed that rely on error estimators based
on Taylor series expansions. While these estimators a) rely on strong
smoothness assumptions and b) may still result in erroneous steps for complex
systems (and thus require step rejection mechanisms), we here propose a
data-driven time stepping scheme based on machine learning, and more
specifically on reinforcement learning (RL) and meta-learning. First, one or
several (in the case of non-smooth or hybrid systems) base learners are trained
using RL. Then, a meta-learner is trained which (depending on the system state)
selects the base learner that appears to be optimal for the current situation.
Several examples including both smooth and non-smooth problems demonstrate the
superior performance of our approach over state-of-the-art numerical schemes.
The code is available under https://github.com/lueckem/quadrature-ML.},
author = {Dellnitz, Michael and Hüllermeier, Eyke and Lücke, Marvin and Ober-Blöbaum, Sina and Offen, Christian and Peitz, Sebastian and Pfannschmidt, Karlson},
booktitle = {arXiv:2104.03562},
title = {{Efficient time stepping for numerical integration using reinforcement learning}},
year = {2021},
}
@article{16295,
abstract = {It is a challenging task to identify the objectives on which a certain decision was based, in particular if several, potentially conflicting criteria are equally important and a continuous set of optimal compromise decisions exists. This task can be understood as the inverse problem of multiobjective optimization, where the goal is to find the objective function vector of a given Pareto set. To this end, we present a method to construct the objective function vector of an unconstrained multiobjective optimization problem (MOP) such that the Pareto critical set contains a given set of data points with prescribed KKT multipliers. If such an MOP can not be found, then the method instead produces an MOP whose Pareto critical set is at least close to the data points. The key idea is to consider the objective function vector in the multiobjective KKT conditions as variable and then search for the objectives that minimize the Euclidean norm of the resulting system of equations. By expressing the objectives in a finite-dimensional basis, we transform this problem into a homogeneous, linear system of equations that can be solved efficiently. Potential applications of this approach include the identification of objectives (both from clean and noisy data) and the construction of surrogate models for expensive MOPs.},
author = {Gebken, Bennet and Peitz, Sebastian},
journal = {Journal of Global Optimization},
pages = {3--29},
publisher = {Springer},
title = {{Inverse multiobjective optimization: Inferring decision criteria from data}},
doi = {10.1007/s10898-020-00983-z},
volume = {80},
year = {2021},
}
@unpublished{21199,
abstract = {As in almost every other branch of science, the major advances in data
science and machine learning have also resulted in significant improvements
regarding the modeling and simulation of nonlinear dynamical systems. It is
nowadays possible to make accurate medium to long-term predictions of highly
complex systems such as the weather, the dynamics within a nuclear fusion
reactor, of disease models or the stock market in a very efficient manner. In
many cases, predictive methods are advertised to ultimately be useful for
control, as the control of high-dimensional nonlinear systems is an engineering
grand challenge with huge potential in areas such as clean and efficient energy
production, or the development of advanced medical devices. However, the
question of how to use a predictive model for control is often left unanswered
due to the associated challenges, namely a significantly higher system
complexity, the requirement of much larger data sets and an increased and often
problem-specific modeling effort. To solve these issues, we present a universal
framework (which we call QuaSiModO:
Quantization-Simulation-Modeling-Optimization) to transform arbitrary
predictive models into control systems and use them for feedback control. The
advantages of our approach are a linear increase in data requirements with
respect to the control dimension, performance guarantees that rely exclusively
on the accuracy of the predictive model, and only little prior knowledge
requirements in control theory to solve complex control problems. In particular
the latter point is of key importance to enable a large number of researchers
and practitioners to exploit the ever increasing capabilities of predictive
models for control in a straight-forward and systematic fashion.},
author = {Peitz, Sebastian and Bieker, Katharina},
booktitle = {arXiv:2102.04722},
title = {{On the Universal Transformation of Data-Driven Models to Control Systems}},
year = {2021},
}
@article{16867,
abstract = {In this article, we present an efficient descent method for locally Lipschitz
continuous multiobjective optimization problems (MOPs). The method is realized
by combining a theoretical result regarding the computation of descent
directions for nonsmooth MOPs with a practical method to approximate the
subdifferentials of the objective functions. We show convergence to points
which satisfy a necessary condition for Pareto optimality. Using a set of test
problems, we compare our method to the multiobjective proximal bundle method by
M\"akel\"a. The results indicate that our method is competitive while being
easier to implement. While the number of objective function evaluations is
larger, the overall number of subgradient evaluations is lower. Finally, we
show that our method can be combined with a subdivision algorithm to compute
entire Pareto sets of nonsmooth MOPs.},
author = {Gebken, Bennet and Peitz, Sebastian},
journal = {Journal of Optimization Theory and Applications},
pages = {696--723},
title = {{An efficient descent method for locally Lipschitz multiobjective optimization problems}},
doi = {10.1007/s10957-020-01803-w},
volume = {188},
year = {2021},
}
@article{21195,
author = {Goelz, Christian and Mora, Karin and Stroehlein, Julia Kristin and Haase, Franziska Katharina and Dellnitz, Michael and Reinsberger, Claus and Vieluf, Solveig},
journal = {Cognitive Neurodynamics},
title = {{Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults}},
doi = {10.1007/s11571-020-09656-9},
year = {2021},
}
@article{16294,
abstract = {Model predictive control is a prominent approach to construct a feedback
control loop for dynamical systems. Due to real-time constraints, the major
challenge in MPC is to solve model-based optimal control problems in a very
short amount of time. For linear-quadratic problems, Bemporad et al. have
proposed an explicit formulation where the underlying optimization problems are
solved a priori in an offline phase. In this article, we present an extension
of this concept in two significant ways. We consider nonlinear problems and -
more importantly - problems with multiple conflicting objective functions. In
the offline phase, we build a library of Pareto optimal solutions from which we
then obtain a valid compromise solution in the online phase according to a
decision maker's preference. Since the standard multi-parametric programming
approach is no longer valid in this situation, we instead use interpolation
between different entries of the library. To reduce the number of problems that
have to be solved in the offline phase, we exploit symmetries in the dynamical
system and the corresponding multiobjective optimal control problem. The
results are verified using two different examples from autonomous driving.},
author = {Ober-Blöbaum, Sina and Peitz, Sebastian},
journal = {International Journal of Robust and Nonlinear Control},
number = {2},
pages = {380--403},
title = {{Explicit multiobjective model predictive control for nonlinear systems with symmetries}},
doi = {10.1002/rnc.5281},
volume = {31},
year = {2021},
}
@unpublished{21572,
author = {Ridderbusch, Steffen and Offen, Christian and Ober-Blöbaum, Sina and Goulart, Paul},
title = {{Learning ODE Models with Qualitative Structure Using Gaussian Processes }},
year = {2021},
}
@article{21337,
abstract = {We present a flexible trust region descend algorithm for unconstrained and
convexly constrained multiobjective optimization problems. It is targeted at
heterogeneous and expensive problems, i.e., problems that have at least one
objective function that is computationally expensive. The method is
derivative-free in the sense that neither need derivative information be
available for the expensive objectives nor are gradients approximated using
repeated function evaluations as is the case in finite-difference methods.
Instead, a multiobjective trust region approach is used that works similarly to
its well-known scalar pendants. Local surrogate models constructed from
evaluation data of the true objective functions are employed to compute
possible descent directions. In contrast to existing multiobjective trust
region algorithms, these surrogates are not polynomial but carefully
constructed radial basis function networks. This has the important advantage
that the number of data points scales linearly with the parameter space
dimension. The local models qualify as fully linear and the corresponding
general scalar framework is adapted for problems with multiple objectives.
Convergence to Pareto critical points is proven and numerical examples
illustrate our findings.},
author = {Berkemeier, Manuel Bastian and Peitz, Sebastian},
issn = {2297-8747},
journal = {Mathematical and Computational Applications},
number = {2},
title = {{Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models}},
doi = {10.3390/mca26020031},
volume = {26},
year = {2021},
}
@article{21820,
abstract = {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 diffusion processes, a general procedure to find effective equations is the conditioning approach. In this paper, we are interested in the spectrum of the generator of the resulting effective dynamics, and how it compares to the spectrum of the full generator. We prove a new relative error bound in terms of the eigenfunction approximation error for reversible systems. We also present numerical examples indicating that, if Kramers–Moyal (KM) type approximations are used to compute the spectrum of the reduced generator, it seems largely insensitive to the time window used for the KM estimators. We analyze the implications of these observations for systems driven by underdamped Langevin dynamics, and show how meaningful effective dynamics can be defined in this setting.},
author = {Nüske, Feliks and Koltai, Péter and Boninsegna, Lorenzo and Clementi, Cecilia},
issn = {1099-4300},
journal = {Entropy},
title = {{Spectral Properties of Effective Dynamics from Conditional Expectations}},
doi = {10.3390/e23020134},
year = {2021},
}
@article{16961,
author = {Liebendörfer, Michael and Göller, Robin and Biehler, Rolf and Hochmuth, Reinhard and Kortemeyer, Jörg and Ostsieker, Laura and Rode, Jana and Schaper, Niclas},
issn = {0173-5322},
journal = {Journal für Mathematik-Didaktik},
title = {{LimSt – Ein Fragebogen zur Erhebung von Lernstrategien im mathematikhaltigen Studium}},
doi = {10.1007/s13138-020-00167-y},
year = {2020},
}
@article{16290,
abstract = {The control of complex systems is of critical importance in many branches of science, engineering, and industry, many of which are governed by nonlinear partial differential equations. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high- dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems.We present a novel deep learning modelpredictive control framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system, which are then embedded into an MPC framework to construct a feedback loop. In order to lower the data requirements and to improve the prediction accuracy and thus the control performance, incoming sensor data are used to update the RNN online. The results are validated using varying fluid flow examples of increasing complexity.},
author = {Bieker, Katharina and Peitz, Sebastian and Brunton, Steven L. and Kutz, J. Nathan and Dellnitz, Michael},
issn = {0935-4964},
journal = {Theoretical and Computational Fluid Dynamics},
pages = {577–591},
title = {{Deep model predictive flow control with limited sensor data and online learning}},
doi = {10.1007/s00162-020-00520-4},
volume = {34},
year = {2020},
}
@unpublished{19941,
abstract = {In backward error analysis, an approximate solution to an equation is
compared to the exact solution to a nearby "modified" equation. In numerical
ordinary differential equations, the two agree up to any power of the step
size. If the differential equation has a geometric property then the modified
equation may share it. In this way, known properties of differential equations
can be applied to the approximation. But for partial differential equations,
the known modified equations are of higher order, limiting applicability of the
theory. Therefore, we study symmetric solutions of discretized partial
differential equations that arise from a discrete variational principle. These
symmetric solutions obey infinite-dimensional functional equations. We show
that these equations admit second-order modified equations which are
Hamiltonian and also possess first-order Lagrangians in modified coordinates.
The modified equation and its associated structures are computed explicitly for
the case of rotating travelling waves in the nonlinear wave equation.},
author = {McLachlan, Robert I and Offen, Christian},
booktitle = {arXiv:2006.14172},
title = {{Backward error analysis for variational discretisations of partial differential equations}},
year = {2020},
}
@article{19939,
author = {Kreusser, Lisa Maria and McLachlan, Robert I and Offen, Christian},
issn = {0951-7715},
journal = {Nonlinearity},
number = {5},
pages = {2335--2363},
title = {{Detection of high codimensional bifurcations in variational PDEs}},
doi = {10.1088/1361-6544/ab7293},
volume = {33},
year = {2020},
}
@article{16288,
abstract = {We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition). This approach is applicable to deterministic and stochastic dynamical systems. It can be used for computing eigenvalues, eigenfunctions, and modes of the generator and for system identification. In addition to learning the governing equations of deterministic systems, which then reduces to SINDy (sparse identification of nonlinear dynamics), it is possible to identify the drift and diffusion terms of stochastic differential equations from data. Moreover, we apply gEDMD to derive coarse-grained models of high-dimensional systems, and also to determine efficient model predictive control strategies. We highlight relationships with other methods and demonstrate the efficacy of the proposed methods using several guiding examples and prototypical molecular dynamics problems.},
author = {Klus, Stefan and Nüske, Feliks and Peitz, Sebastian and Niemann, Jan-Hendrik and Clementi, Cecilia and Schütte, Christof},
issn = {0167-2789},
journal = {Physica D: Nonlinear Phenomena},
title = {{Data-driven approximation of the Koopman generator: Model reduction, system identification, and control}},
doi = {10.1016/j.physd.2020.132416},
volume = {406},
year = {2020},
}
@article{21819,
abstract = {Many dimensionality and model reduction techniques rely on estimating dominant eigenfunctions of associated dynamical operators from data. Important examples include the Koopman operator and its generator, but also the Schrödinger operator. We propose a kernel-based method for the approximation of differential operators in reproducing kernel Hilbert spaces and show how eigenfunctions can be estimated by solving auxiliary matrix eigenvalue problems. The resulting algorithms are applied to molecular dynamics and quantum chemistry examples. Furthermore, we exploit that, under certain conditions, the Schrödinger operator can be transformed into a Kolmogorov backward operator corresponding to a drift-diffusion process and vice versa. This allows us to apply methods developed for the analysis of high-dimensional stochastic differential equations to quantum mechanical systems.},
author = {Klus, Stefan and Nüske, Feliks and Hamzi, Boumediene},
issn = {1099-4300},
journal = {Entropy},
title = {{Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator}},
doi = {10.3390/e22070722},
year = {2020},
}
@article{10596,
abstract = {Multi-objective optimization is an active field of research that has many applications. Owing to its success and because decision-making processes are becoming more and more complex, there is a recent trend for incorporating many objectives into such problems. The challenge with such problems, however, is that the dimensions of the solution sets—the so-called Pareto sets and fronts—grow with the number of objectives. It is thus no longer possible to compute or to approximate the entire solution set of a given problem that contains many (e.g. more than three) objectives. On the other hand, the computation of single solutions (e.g. via scalarization methods) leads to unsatisfying results in many cases, even if user preferences are incorporated. In this article, the Pareto Explorer tool is presented—a global/local exploration tool for the treatment of many-objective optimization problems (MaOPs). In the first step, a solution of the problem is computed via a global search algorithm that ideally already includes user preferences. In the second step, a local search along the Pareto set/front of the given MaOP is performed in user specified directions. For this, several continuation-like procedures are proposed that can incorporate preferences defined in decision, objective, or in weight space. The applicability and usefulness of Pareto Explorer is demonstrated on benchmark problems as well as on an application from industrial laundry design.},
author = {Schütze, Oliver and Cuate, Oliver and Martín, Adanay and Peitz, Sebastian and Dellnitz, Michael},
issn = {0305-215X},
journal = {Engineering Optimization},
number = {5},
pages = {832--855},
title = {{Pareto Explorer: a global/local exploration tool for many-objective optimization problems}},
doi = {10.1080/0305215x.2019.1617286},
volume = {52},
year = {2020},
}
@inbook{16289,
abstract = {In the development of model predictive controllers for PDE-constrained problems, the use of reduced order models is essential to enable real-time applicability. Besides local linearization approaches, proper orthogonal decomposition (POD) has been most widely used in the past in order to derive such models. Due to the huge advances concerning both theory as well as the numerical approximation, a very promising alternative based on the Koopman operator has recently emerged. In this chapter, we present two control strategies for model predictive control of nonlinear PDEs using data-efficient approximations of the Koopman operator. In the first one, the dynamic control system is replaced by a small number of autonomous systems with different yet constant inputs. The control problem is consequently transformed into a switching problem. In the second approach, a bilinear surrogate model is obtained via a convex combination of these autonomous systems. Using a recent convergence result for extended dynamic mode decomposition (EDMD), convergence of the reduced objective function can be shown. We study the properties of these two strategies with respect to solution quality, data requirements, and complexity of the resulting optimization problem using the 1-dimensional Burgers equation and the 2-dimensional Navier–Stokes equations as examples. Finally, an extension for online adaptivity is presented.},
author = {Peitz, Sebastian and Klus, Stefan},
booktitle = {Lecture Notes in Control and Information Sciences},
isbn = {9783030357122},
issn = {0170-8643},
pages = {257--282},
publisher = {Springer},
title = {{Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator}},
doi = {10.1007/978-3-030-35713-9_10},
volume = {484},
year = {2020},
}
@inbook{13108,
abstract = {Diagrammatisches Schlie{\ss}en wird im Zusammenhang mit dem Lernen von Mathmematik und ihrer Symbolsprache als wesentliche Theorie der Wissenskonstruktion diskutiert. Dabei wird h{\"{a}}ufig davon ausgegangen, dass die Wissenskonstruktion im Sinne diagrammatischen Schlie{\ss}ens erfolgt. Deskriptive Rekonstruktionen diagrammatischen Schlie{\ss}ens bei Lernenden stellen jedoch ein Desiderat der mathematikdidaktischen Forschung dar. Der vorliegende Beitrag befasst sich mit der Fragestellung, wie sich diagrammatisches Schlie{\ss}en bei Lernenden rekonstruieren l{\"{a}}sst. Als m{\"{o}}gliche Werkzeuge f{\"{u}}r eine solche Rekonstruktion werden Toulmins Argumentationsschema und Vergnauds Schema-Begriff exemplarisch angewandt, um das diagrammatische Schlie{\ss}en eines Sch{\"{u}}lerpaars beim Einstieg in die Subtraktion negativer Zahlen zu rekonstruieren. Abschlie{\ss}end wird die tats{\"{a}}chliche Eignung der beiden Ans{\"{a}}tze zur Rekonstruktion diagrammatischen Schlie{\ss}ens diskutiert.},
author = {Schumacher, Jan and Rezat, Sebastian},
booktitle = {Zeichen und Sprache im Mathematikunterricht},
editor = {Kadunz, Gert},
publisher = {Springer},
title = {{Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge}},
doi = {10.1007/978-3-662-61194-4_5},
year = {2020},
}
@article{16710,
abstract = {In this work we present a set-oriented path following method for the computation of relative global
attractors of parameter-dependent dynamical systems. We start with an initial approximation of the
relative global attractor for a fixed parameter λ0 computed by a set-oriented subdivision method.
By using previously obtained approximations of the parameter-dependent relative global attractor
we can track it with respect to a one-dimensional parameter λ > λ0 without restarting the whole
subdivision procedure. We illustrate the feasibility of the set-oriented path following method by
exploring the dynamics in low-dimensional models for shear flows during the transition to turbulence
and of large-scale atmospheric regime changes .
},
author = {Gerlach, Raphael and Ziessler, Adrian and Eckhardt, Bruno and Dellnitz, Michael},
issn = {1536-0040},
journal = {SIAM Journal on Applied Dynamical Systems},
pages = {705--723},
title = {{A Set-Oriented Path Following Method for the Approximation of Parameter Dependent Attractors}},
doi = {10.1137/19m1247139},
year = {2020},
}
@article{16309,
abstract = {In recent years, the success of the Koopman operator in dynamical systems
analysis has also fueled the development of Koopman operator-based control
frameworks. In order to preserve the relatively low data requirements for an
approximation via Dynamic Mode Decomposition, a quantization approach was
recently proposed in [Peitz & Klus, Automatica 106, 2019]. This way, control
of nonlinear dynamical systems can be realized by means of switched systems
techniques, using only a finite set of autonomous Koopman operator-based
reduced models. These individual systems can be approximated very efficiently
from data. The main idea is to transform a control system into a set of
autonomous systems for which the optimal switching sequence has to be computed.
In this article, we extend these results to continuous control inputs using
relaxation. This way, we combine the advantages of the data efficiency of
approximating a finite set of autonomous systems with continuous controls. We
show that when using the Koopman generator, this relaxation --- realized by
linear interpolation between two operators --- does not introduce any error for
control affine systems. This allows us to control high-dimensional nonlinear
systems using bilinear, low-dimensional surrogate models. The efficiency of the
proposed approach is demonstrated using several examples with increasing
complexity, from the Duffing oscillator to the chaotic fluidic pinball.},
author = {Peitz, Sebastian and Otto, Samuel E. and Rowley, Clarence W.},
journal = {SIAM Journal on Applied Dynamical Systems},
number = {3},
pages = {2162--2193},
title = {{Data-Driven Model Predictive Control using Interpolated Koopman Generators}},
doi = {10.1137/20M1325678},
volume = {19},
year = {2020},
}