Publications (30)
On the dynamics of the mean-field polaron in the weak-coupling limit
Marcel Griesemer, Jochen Schmid, Guido Schneider
We consider the dynamics of the mean-field polaron in the weak-coupling limit of vanishing electron-phonon interaction, . This is a singular limit formally leadi…
Approximation, characterization, and continuity of multivariate monotonic regression functions
Jochen Schmid
We deal with monotonic regression of multivariate functions on a compact rectangular domain in , where monotonicity is understood in a gener…
Kato's theorem on the integration of non-autonomous linear evolution equations
Jochen Schmid, Marcel Griesemer
This paper is devoted to a comparison of early works of Kato and Yosida on the integration of non-autonomous linear evolution equations in Banach space, where the…
Adiabatic theorems with and without spectral gap condition for non-semisimple spectral values
Jochen Schmid
We establish adiabatic theorems with and without spectral gap condition for general operators with possibly time-dependent domains in a Banach space…
Incorporating Shape Knowledge into Regression Models
Miltiadis Poursanidis, Patrick Link, Jochen Schmid +1
Informed learning is an emerging field in machine learning that aims to compensate for insufficient data with prior knowledge. Shape knowledge covers many types of prior knowledge…
Well-posedness of non-autonomous linear evolution equations for generators whose commutators are scalar
Jochen Schmid
We prove the well-posedness of non-autonomous linear evolution equations for generators whose pairwise commutators are complex scalars and, in addit…
Calibrated simplex-mapping classification
Raoul Heese, Jochen Schmid, MichaÅ Walczak +1
We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers…
DiffStyleTS: Diffusion Model for Style Transfer in Time Series
Mayank Nagda, Phil Ostheimer, Justus Arweiler +13
Style transfer combines the content of one signal with the style of another. It supports applications such as data augmentation and scenario simulation, helping machine learning mo…
Weak input-to-state stability: characterizations and counterexamples
Jochen Schmid
We establish characterizations of weak input-to-state stability for abstract dynamical systems with inputs, which are similar to characterizations of uniform and of strong input-to…
Compensating data shortages in manufacturing with monotonicity knowledge
Martin von Kurnatowski, Jochen Schmid, Patrick Link +5
Optimization in engineering requires appropriate models. In this article, a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the fo…
Stabilization of port-Hamiltonian systems with discontinuous energy densities
Jochen Schmid
We establish an exponential stabilization result for linear port-Hamiltonian systems of first order with quite general, not necessarily continuous, energy densities. In fact, we ha…
Well-posedness and stability of non-autonomous semilinear input-output systems
Jochen Schmid
We establish well-posedness results for non-autonomous semilinear input-output systems, the central assumption being the scattering-passivity of the considered semilinear system. W…
Adaptive discretization algorithms for locally optimal experimental design
Jochen Schmid, Philipp Seufert, Michael Bortz
We develop adaptive discretization algorithms for locally optimal experimental design of nonlinear prediction models. With these algorithms, we refine and improve a pertinent state…
Adiabatensätze mit und ohne Spektrallückenbedingung
Jochen Schmid
In this work we generalize some of the previously known adiabatic theorems to situations with non-unitary evolutions in Banach spaces. We prove adiabatic theorems with uniform gap…
Stabilization of port-Hamiltonian systems by nonlinear boundary control in the presence of disturbances
Jochen Schmid, Hans Zwart
In this paper, we are concerned with the stabilization of linear port-Hamiltonian systems of arbitrary order on a bounded -dimensional spatial domain .…
Infinite-time admissibility under compact perturbations
Jochen Schmid
We investigate the behavior of infinite-time admissibility under compact perturbations. We show, by means of two completely different examples, that infinite-time admissibility is…
Adiabatic theorems for general linear operators with time-dependent domains
Jochen Schmid
We establish adiabatic theorems with and without spectral gap condition for general -- typically dissipative -- linear operators with time-dependent…
A local input-to-state stability result w.r.t. attractors of nonlinear reaction-diffusion equations
Sergey Dashkovskiy, Oleksiy V. Kapustyan, Jochen Schmid
We establish the local input-to-state stability of a large class of disturbed nonlinear reaction-diffusion equations w.r.t. the global attractor of the respective undisturbed syste…
An equation-based batch distillation simulation to evaluate the effect of multiplicities in thermodynamic activity coefficients
Jennifer Werner, Jochen Schmid, Lorenz T. Biegler +1
In this paper, we investigate the influence of multiplicities in activity coefficients on batch distillation processes. In order to do so, we develop a rigorous simulation of batch…
Well-posedness of non-autonomous linear evolution equations in uniformly convex spaces
Jochen Schmid, Marcel Griesemer
This paper addresses the problem of wellposedness of non-autonomous linear evolution equations in uniformly convex Banach spaces. We assume that $A(t):D \subset X\…
Adiabatic theorems for general linear operators with time-independent domains
Jochen Schmid
We establish adiabatic theorems with and without spectral gap condition for general -- typically dissipative -- linear operators with time-independe…
Cubature-based uncertainty estimation for nonlinear regression models
Martin Bubel, Jochen Schmid, Maximilian Carmesin +3
Calibrating model parameters to measured data by minimizing loss functions is an important step in obtaining realistic predictions from model-based approaches, e.g., for process op…
Sequential optimal experimental design for vapor-liquid equilibrium modeling
Martin Bubel, Jochen Schmid, Volodymyr Kozachynskyi +2
We propose a general methodology of sequential locally optimal design of experiments for explicit or implicit nonlinear models, as they abound in chemical engineering and, in parti…
An adaptive discretization algorithm for locally optimal experimental design with constraints
Jochen Schmid, Philipp Seufert, Jan Schwientek +2
We develop a novel iterative algorithm for locally optimal experimental design under constraints, like budget or performance constraints. It is an adaptive discretization algorithm…
Asymptotic gain results for attractors of semilinear systems
Jochen Schmid, Oleksiy V. Kapustyan, Sergey Dashkovskiy
We establish asymptotic gain along with input-to-state practical stability results for disturbed semilinear systems w.r.t. the global attractor of the respective undisturbed system…
Approximate solutions of convex semi-infinite optimization problems in finitely many iterations
Jochen Schmid, Miltiadis Poursanidis
We develop two adaptive discretization algorithms for convex semi-infinite optimization, which terminate after finitely many iterations at approximate solutions of arbitrary precis…
Simulation and optimal control of the Williams-Otto process using Pyomo
Jochen Schmid, Katrin Teichert, Moncef Chioua +2
We illustrate the advantages the high-level open-source software package Pyomo has in rapidly setting up and solving dynamic simulation and optimization problems. In order to do so…
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing
Patrick Link, Miltiadis Poursanidis, Jochen Schmid +4
Increasing digitalization enables the use of machine learning methods for analyzing and optimizing manufacturing processes. A main application of machine learning is the constructi…
Accelerating nanodrug development in continuous flow systems using informed prediction models based on low-cost surrogate nanoparticles
Kai Dahms, Eilien Heinrich, Jochen Schmid +3
The development of nanotherapeutics often involves extensive empirical optimization due to the sensitivity of nanoparticle properties, such as size and polydispersity index (PDI),…
Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection
Jennifer Werner, Justus Arweiler, Indra Jungjohann +4
Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated training datasets that are rare…