8 citations · 9 across the 15 of their papers we have counts for
16 papers
Towards an Optimal Control Foundation of Early Exits - Turnpikes and ResNets
Jens Püttschneider, Simon Heilig, Asja Fischer +1
The feed-forward propagation through neural networks is crucial for training and inference. To trade off efficiency and accuracy, it has been proposed to consider intermediate outp…
Fast Risk Certification of Candidate Trajectories under Uncertain Time-Varying Constraints
Srimanta Santra, Oleksii Molodchyk, Matti Noack +1
This paper studies the certification of a fixed candidate trajectory on a finite certification grid under parametric uncertainty. For each constraint-time pair, we define a scalar…
Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study
Guanru Pan, Dirk Reinhardt, Sebastien Gros +1
This paper presents a data-driven framework for uncertainty propagation under unmeasured or statistically unmodeled (unstructured) disturbances. We consider residual disturbances,…
Towards Closed-loop Stability of Nonlinear Receding Horizon Games
Sophie Hall, Florian Dörfler, Timm Faulwasser
We analyze Receding Horizon Games without any MPC-like terminal ingredients. We show that recursive feasibility can be inferred from the turnpike phenomenon under mild assumptions.…
On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
Johannes Teutsch, Oleksii Molodchyk, Marion Leibold +2
Providing non-conservative uncertainty quantification for function estimates derived from noisy observations remains a fundamental challenge in statistical machine learning, partic…
Data-Driven Power Flow for Radial Distribution Networks with Sparse Real-Time Data
Oleksii Molodchyk, Omid Mokhtari, Samuel Chevalier +2
Real-time control of distribution networks requires accurate information about the system state. In practice, however, such information is difficult to obtain because real-time mea…
Flexibility aggregation via set projection for distribution grids with multiple interconnections
Maísa Beraldo Bandeira, Alexander Engelmann, Timm Faulwasser
With the increasing number of flexible energy devices in distribution grids, coordination between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) beco…
Towards grid-aware multi-period flexibility aggregation - A constrained zonotope approach
Maurice Raetsch, Maísa Beraldo Bandeira, Christian Rehtanz +2
Aggregation schemes provide a means to reduce the computational complexity of power system operation by reducing the number of devices that are considered individually. This can be…
Towards Stochastic (N-1)-Secure Redispatch
Oleksii Molodchyk, Hendrik Drögehorn, Martin Lindner +2
The intermittent nature of renewable power availability is one of the major sources of uncertainty in power systems. While markets can guarantee that the demand is covered by the a…
Towards an Optimal Control Perspective of ResNet Training
Jens Püttschneider, Simon Heilig, Asja Fischer +1
We propose a training formulation for ResNets reflecting an optimal control problem that is applicable for standard architectures and general loss functions. We suggest bridging bo…
Exact Time-Varying Turnpikes for Dynamic Operation of District Heating Networks
Max Rose, Hannes Gernandt, Timm Faulwasser +1
District heating networks (DHNs) are crucial for decarbonizing the heating sector. Yet, their efficient and reliable operation requires the coordination of multiple heat producers…
Towards Data-Driven Multi-Stage OPF
Oleksii Molodchyk, Philipp Schmitz, Alexander Engelmann +2
The operation of large-scale power systems is usually scheduled ahead via numerical optimization. However, this requires models of grid topology, line parameters, and bus specifica…
Towards safe Bayesian optimization with Wiener kernel regression
Oleksii Molodchyk, Johannes Teutsch, Timm Faulwasser
Bayesian Optimization (BO) is a data-driven strategy for minimizing/maximizing black-box functions based on probabilistic surrogate models. In the presence of safety constraints, t…
Exploring the Links between the Fundamental Lemma and Kernel Regression
Oleksii Molodchyk, Timm Faulwasser
Generalizations and variations of the fundamental lemma by Willems et al. are an active topic of recent research. In this note, we explore and formalize the links between kernel re…
Decentralized Real-Time Iterations for Distributed NMPC
Gösta Stomberg, Alexander Engelmann, Moritz Diehl +1
This article presents a Real-Time Iteration (RTI) scheme for distributed Nonlinear Model Predictive Control (NMPC). The scheme transfers the well-known RTI approach, a key enabler…
Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty
T. Faulwasser, O. Molodchyk
Gaussian Processes (GPs) are a versatile method that enables different approaches towards learning for dynamics and control. Gaussianity assumptions appear in two dimensions in GPs…