Results 11 to 20 of about 8,619,235 (382)
International Journal of Theoretical Physics, 2016 34 pages, 8 figures.
Giacomo Mauro D’Ariano
core +8 more sources
Unleashing the full power of LHCb to probe stealth new physics [PDF]
Reports on progress in physics. Physical Society, 2021 In this paper, we describe the potential of the LHCb experiment to detect stealth physics. This refers to dynamics beyond the standard model that would elude searches that focus on energetic objects or precision measurements of known processes.
M. Borsato +40 more
semanticscholar +2 more sources
Nature Reviews Physics, 2022 Getting the most from power-law-type data can be challenging. James Sethna points out some of the pitfalls in studying power laws arising from emergent scale invariance, as well as important opportunities.
J. Sethna
openaire +3 more sources
The physics of power systems operation [PDF]
EPJ Web of Conferences, 2015 The article explains the operation of power systems from the point of view of physics. Physicists imagine things, rather than in terms of impedances and circuits, in terms of fields and energy conversions. The account is concrete and simple.
Ohler C.
doaj +2 more sources
Physics of the power corrections in QCD [PDF]
Surveys in High Energy Physics, 2000 We review the physics of the power corrections to the parton model. In the first part, we consider the power corrections which characterize the infrared sensitivity of Feynman graphs when the contribution of short distances dominates. The second part is devoted to the hypothetical power corrections associated with nonperturbative effects at small ...
Gubarev, F. V. +2 more
openaire +4 more sources
Physics-guided Deep Learning for Power System State Estimation
Journal of Modern Power Systems and Clean Energy, 2020 In the past decade, dramatic progress has been made in the field of machine learning. This paper explores the possibility of applying deep learning in power system state estimation.
Lei Wang, Qun Zhou, Shuangshuang Jin
doaj +2 more sources
Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support [PDF]
Electric power systems research, 2022 The application of deep learning methods to speed up the resolution of challenging power flow problems has recently shown very encouraging results. However, power system dynamics are not snap-shot, steady-state operations.
M. Mohammadian +2 more
semanticscholar +1 more source
Physically Flexible Ultralow-Power Wireless Sensor
IEEE Transactions on Instrumentation and Measurement, 2022 The key challenges of local sensor networks are in supporting high sensor density, information security, physical size, and especially energy efficiency at a level that could eliminate the need for batteries or external power supplies. This article presents a novel scheme that answers all issues at the cost of minor information losses in low data rate ...
Caffrey, Colm Mc +4 more
openaire +4 more sources
Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning
IEEE transactions on power electronics, 2022 Physics-informed machine learning (PIML) has been emerging as a promising tool for applications with domain knowledge and physical models. To uncover its potentials in power electronics, this article proposes a PIML-based parameter estimation method ...
Shuai Zhao +3 more
semanticscholar +1 more source
Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow [PDF]
IEEE International Conference on Smart Grid Communications, 2021 Physics-informed neural networks exploit the existing models of the underlying physical systems to generate higher accuracy results with fewer data.
Rahul Nellikkath +1 more
semanticscholar +1 more source