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Jasmin Hörter

Publications and source records attributed to Jasmin Hörter.

4 recordsLinked to original sources
arXiv2026

Differentiable Power-Flow Optimization

With the rise of renewable energy sources and their high variability in generation, the management of power grids becomes increasingly complex and computationally demanding. Conventional AC-power-flow simulations, which use the Newton-Raphson (NR) method, suffer from poor scalability, making them impractical for emerging use cases such as joint transmission-distribution modeling and global grid analysis. At the same time, purely data-driven surrogate models lack physical guarantees and may violate fundamental constraints. In this work, we propose Differentiable Power-Flow (DPF), a reformulation of the AC power-flow problem as a differentiable simulation. DPF enables end-to-end gradient propagation from the physical power mismatches to the underlying simulation parameters, thereby allowing these parameters to be identified efficiently using gradient-based optimization. We demonstrate that DPF provides a scalable alternative to NR by leveraging GPU acceleration, sparse tensor representations, and batching capabilities available in modern machine-learning frameworks such as PyTorch. DPF is especially suited as a tool for time-series analyses due to its efficient reuse of previous solutions, for N-1 contingency-analyses due to its ability to process cases in batches, and as a screening tool by leveraging its speed and early stopping capability. The code is available in the authors' code repository.

cs.AI↗
arXiv2025

Model Fusion via Neuron Transplantation

Ensemble learning is a widespread technique to improve the prediction performance of neural networks. However, it comes at the price of increased memory and inference time. In this work we propose a novel model fusion technique called \emph{Neuron Transplantation (NT)} in which we fuse an ensemble of models by transplanting important neurons from all ensemble members into the vacant space obtained by pruning insignificant neurons. An initial loss in performance post-transplantation can be quickly recovered via fine-tuning, consistently outperforming individual ensemble members of the same model capacity and architecture. Furthermore, NT enables all the ensemble members to be jointly pruned and jointly trained in a combined model. Comparing it to alignment-based averaging (like Optimal-Transport-fusion), it requires less fine-tuning than the corresponding OT-fused model, the fusion itself is faster and requires less memory, while the resulting model performance is comparable or better. The code is available under the following link: https://github.com/masterbaer/neuron-transplantation.

cs.LG↗
arXiv2022

Rigidity of $\varepsilon$-harmonic maps of low degree

In 1981, Sacks and Uhlenbeck introduced their famous $α$-energy as a way to approximate the Dirichlet energy and produce harmonic maps from surfaces into Riemannian manifolds. However, the second and third authors together with Malchiodi ([11],[12]) showed that for maps between two-spheres this method does not capture every harmonic map. They established a gap theorem for $α$-harmonic maps of degree zero and also showed that below a certain energy bound $α$-harmonic maps of degree one are rotations. We establish similar results for $\varepsilon$-harmonic maps $u_\varepsilon \colon S^2\rightarrow S^2$, which are critical points of the $\varepsilon$-energy introduced by the second author in [9]. In particular, we similarly show that $\varepsilon$-harmonic maps of degree zero with energy below $8π$ are constant and that maps of degree $\pm 1$ with energy below $12π$ are of the form $Rx$ with $R\in O(3)$. Moreover, we construct non-trivial $\varepsilon$-harmonic maps of degree zero with energy $> 8π$.

math.DG↗