Zobrazeno 1 - 10
of 12 759
pro vyhledávání: '"A, Dahmen"'
Time-series information needs to be incorporated into energy system optimization to account for the uncertainty of renewable energy sources. Typically, time-series aggregation methods are used to reduce historical data to a few representative scenari
Externí odkaz:
http://arxiv.org/abs/2411.14320
Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that synaptic strengths depend on the type of neurons they connect, but are
Externí odkaz:
http://arxiv.org/abs/2411.06802
Autor:
Wu, Yueh-Chun, Halász, Gábor B., Damron, Joshua T., Gai, Zheng, Zhao, Huan, Sun, Yuxin, Dahmen, Karin A, Sohn, Changhee, Carlson, Erica W., Hua, Chengyun, Lin, Shan, Song, Jeongkeun, Lee, Ho Nyung, Lawrie, Benjamin J.
Thermally driven transitions between ferromagnetic and paramagnetic phases are characterized by critical behavior with divergent susceptibilities, long-range correlations, and spin dynamics that can span kHz to GHz scales as the material approaches t
Externí odkaz:
http://arxiv.org/abs/2410.19158
Number fields and their rings of integers, which generalize the rational numbers and the integers, are foundational objects in number theory. There are several computer algebra systems and databases concerned with the computational aspects of these.
Externí odkaz:
http://arxiv.org/abs/2409.18030
We show that the Fr\'echet--Lie groups of the form $C^{\infty}(M)\rtimes \mathbb{R}$ resulting from smooth flows on compact manifolds $M$ fail to be locally exponential in several cases: when at least one non-periodic orbit is locally closed, or when
Externí odkaz:
http://arxiv.org/abs/2408.15053
Autor:
Velioglu, Mehmet, Zhai, Song, Rupprecht, Sophia, Mitsos, Alexander, Jupke, Andreas, Dahmen, Manuel
In chemical engineering, process data are expensive to acquire, and complex phenomena are difficult to fully model. We explore the use of physics-informed neural networks (PINNs) for modeling dynamic processes with incomplete mechanistic semi-explici
Externí odkaz:
http://arxiv.org/abs/2406.01528
This paper is about learning the parameter-to-solution map for systems of partial differential equations (PDEs) that depend on a potentially large number of parameters covering all PDE types for which a stable variational formulation (SVF) can be fou
Externí odkaz:
http://arxiv.org/abs/2405.20065
Autor:
Fischer, Kirsten, Lindner, Javed, Dahmen, David, Ringel, Zohar, Krämer, Michael, Helias, Moritz
A key property of neural networks driving their success is their ability to learn features from data. Understanding feature learning from a theoretical viewpoint is an emerging field with many open questions. In this work we capture finite-width effe
Externí odkaz:
http://arxiv.org/abs/2405.10761
We present a method for end-to-end learning of Koopman surrogate models for optimal performance in a specific control task. In contrast to previous contributions that employ standard reinforcement learning (RL) algorithms, we use a training algorithm
Externí odkaz:
http://arxiv.org/abs/2403.14425
Autor:
Germscheid, Sonja H. M., Nilges, Benedikt, von der Assen, Niklas, Mitsos, Alexander, Dahmen, Manuel
This work studies synergies arising from combining industrial demand response and local renewable electricity supply. To this end, we optimize the design of a local electricity generation and storage system with an integrated demand response scheduli
Externí odkaz:
http://arxiv.org/abs/2401.12759