Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Riemer-Sorensen, Signe"'
Autor:
Wu, Jie, Eidnes, Sølve, Jin, Jingzhe, Lie, Halvor, Yin, Decao, Passano, Elizabeth, Sævik, Svein, Riemer-Sorensen, Signe
Publikováno v:
Journal of Fluids and Structures, Volume 116, 2023, 103793
Offshore slender marine structures experience complex and combined load conditions from waves, current and vessel motions that may result in both wave frequency and vortex shedding response patterns. Field measurements often consist of records of env
Externí odkaz:
http://arxiv.org/abs/2406.18611
We present an investigation of how topological data analysis (TDA) can be applied to condition-based monitoring (CBM) of wind turbines for energy generation. TDA is a branch of data analysis focusing on extracting meaningful information from complex
Externí odkaz:
http://arxiv.org/abs/2406.16380
Time-series modeling in process industries faces the challenge of dealing with complex, multi-faceted, and evolving data characteristics. Conventional single model approaches often struggle to capture the interplay of diverse dynamics, resulting in s
Externí odkaz:
http://arxiv.org/abs/2403.02150
We propose a new method for inferring roads from GPS trajectories to map construction sites. This task presents a unique challenge due to the erratic and non-standard movement patterns of construction machinery, which significantly diverge from typic
Externí odkaz:
http://arxiv.org/abs/2402.09919
Publikováno v:
Energy Strategy Reviews Volume 52, March 2024, 101331
The balancing market for power is designed to account for the difference between predicted supply/demand of electricity and the realised supply/demand. However, increased electrification of society changes the consumption patterns, and increased prod
Externí odkaz:
http://arxiv.org/abs/2402.09134
Radio, sub-millimiter and millimeter ground-based telescopes are powerful instruments for studying the gas and dust-rich regions of the Universe that are invisible at optical wavelengths, but the pointing accuracy is crucial for obtaining high-qualit
Externí odkaz:
http://arxiv.org/abs/2402.08589
Deep operator networks (DeepONets, DONs) offer a distinct advantage over traditional neural networks in their ability to be trained on multi-resolution data. This property becomes especially relevant in real-world scenarios where high-resolution meas
Externí odkaz:
http://arxiv.org/abs/2310.02491
Identifying the underlying dynamics of physical systems can be challenging when only provided with observational data. In this work, we consider systems that can be modelled as first-order ordinary differential equations. By assuming a certain pseudo
Externí odkaz:
http://arxiv.org/abs/2305.06920
Deep neural networks are an attractive alternative for simulating complex dynamical systems, as in comparison to traditional scientific computing methods, they offer reduced computational costs during inference and can be trained directly from observ
Externí odkaz:
http://arxiv.org/abs/2303.02243
Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conserving. We introduce a pseudo-Hamiltonian formulation that is a generalizat
Externí odkaz:
http://arxiv.org/abs/2206.02660