Zobrazeno 1 - 10
of 350
pro vyhledávání: '"Geyer, Philipp"'
Symbolic neural networks, such as Kolmogorov-Arnold Networks (KAN), offer a promising approach for integrating prior knowledge with data-driven methods, making them valuable for addressing inverse problems in scientific and engineering domains. This
Externí odkaz:
http://arxiv.org/abs/2411.00800
Autor:
Chen, Xia, Rex, Alexander, Woelke, Janis, Eckert, Christoph, Bensmann, Boris, Hanke-Rauschenbach, Richard, Geyer, Philipp
In this study, we propose to adopt a novel framework, Knowledge-integrated Machine Learning, for advancing Proton Exchange Membrane Water Electrolysis (PEMWE) development. Given the significance of PEMWE in green hydrogen production and the inherent
Externí odkaz:
http://arxiv.org/abs/2404.03660
The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. We investigated the synergetic pattern between the data-driven methods, empirical domain knowledge, and first-principles simulati
Externí odkaz:
http://arxiv.org/abs/2309.11509
Autor:
Chen, Xia, Geyer, Philipp
Despite the digitalization trend and data volume surge, first-principles models (also known as logic-driven, physics-based, rule-based, or knowledge-based models) and data-driven approaches have existed in parallel, mirroring the ongoing AI debate on
Externí odkaz:
http://arxiv.org/abs/2307.06950
Autor:
Pedalino, Sebastian, Galindo, Bruno Ramírez, de Sousa, Tomas, Fein, Yaakov Y., Geyer, Philipp, Gerlich, Stefan, Arndt, Markus
We discuss recent advances towards matter-wave interference experiments with free beams of metallic and dielectric nanoparticles. They require a brilliant source, an efficient detection scheme and a coherent method to divide the de Broglie waves asso
Externí odkaz:
http://arxiv.org/abs/2301.11095
The development of current building energy system operation has benefited from: 1. Informational support from the optimal design through simulation or first-principles models; 2. System load and energy prediction through machine learning (ML). Throug
Externí odkaz:
http://arxiv.org/abs/2302.10179
The demand for a huge amount of data for machine learning (ML) applications is currently a bottleneck in an empirically dominated field. We propose a method to combine prior knowledge with data-driven methods to significantly reduce their data depend
Externí odkaz:
http://arxiv.org/abs/2302.10784
Publikováno v:
Biomedical Signal Processing and Control, 2023
This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 prevalent NN models and their variants across four brain-computer interface
Externí odkaz:
http://arxiv.org/abs/2207.12369
Publikováno v:
Advanced Engineering Informatics 52 (2022): 101627
The performance gap between predicted and actual energy consumption in the building domain remains an unsolved problem in practice. The gap exists differently in both current mainstream methods: the first-principles model and the machine learning (ML
Externí odkaz:
http://arxiv.org/abs/2206.00460
Publikováno v:
Energy and Buildings, 2022
"What-if" questions are intuitively generated and commonly asked during the design process. Engineers and architects need to inherently conduct design decisions, progressing from one phase to another. They either use empirical domain experience, simu
Externí odkaz:
http://arxiv.org/abs/2203.10115