Zobrazeno 1 - 10
of 717
pro vyhledávání: '"Physics-informed machine learning"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated.
Externí odkaz:
https://doaj.org/article/8afafdddc7b945d58ed0f1759471b450
Autor:
Chady Ghnatios, Daniele Di Lorenzo, Victor Champaney, Amine Ammar, Elias Cueto, Francisco Chinesta
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 11, Iss 1, Pp 1-19 (2024)
Abstract Trajectory planning aims at computing an optimal trajectory through the minimization of a cost function. This paper considers four different scenarios: (i) the first concerns a given trajectory on which a cost function is minimized by a acti
Externí odkaz:
https://doaj.org/article/364185a191144ec99108fb26b8e9a3f0
Publikováno v:
Data in Brief, Vol 55, Iss , Pp 110703- (2024)
Real-time monitoring of milling parameters is essential to improve machining efficiency and quality, especially for the workpieces with complex geometry. Its main task is to build the relationship between the parameters and the monitoring data. As th
Externí odkaz:
https://doaj.org/article/3c3309bc0752483284ea25cfab7ec12c
Publikováno v:
IEEE Access, Vol 12, Pp 122426-122436 (2024)
The improvement of fault diagnosis for complex equipment is an important step towards intelligent systems. Unlike component-level fault detection, system-level fault diagnosis presents new challenges, such as the integration of multiple sensors, the
Externí odkaz:
https://doaj.org/article/73cae43ad4764f7a932266016c5a40ba
Autor:
Qing Shen, Yifan Zhou, Peng Zhang, Huanfeng Zhao, Qiang Zhang, Slava Maslennikov, Xiaochuan Luo
Publikováno v:
IEEE Access, Vol 12, Pp 86513-86522 (2024)
Traditional grid analytics heavily rely on accurate power system models, especially dynamic ones for generators, controllers, and loads. However, obtaining comprehensive models is impractical in real operations due to inaccessible parameters and cons
Externí odkaz:
https://doaj.org/article/ed44ce8290c24299908dc1f5e9f25e78
Autor:
Shannon Ryan, Neeraj Mohan Sushma, Arun Kumar AV, Julian Berk, Tahrima Hashem, Santu Rana, Svetha Venkatesh
Publikováno v:
Defence Technology, Vol 31, Iss , Pp 14-26 (2024)
Machine learning (ML) is well suited for the prediction of high-complexity, high-dimensional problems such as those encountered in terminal ballistics. We evaluate the performance of four popular ML-based regression models, extreme gradient boosting
Externí odkaz:
https://doaj.org/article/f8ae746baea649ff92acbd13d22b2430
Publikováno v:
IEEE Access, Vol 12, Pp 4597-4617 (2024)
Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs ( $\mu $ -PMUs), and smart meters. However, large amount of data collected by these devices bring
Externí odkaz:
https://doaj.org/article/d1f7ebf715594df4b931c9914397614c
Autor:
Alessio Alexiadis
Publikováno v:
Frontiers in Nanotechnology, Vol 6 (2024)
This article presents an in-depth analysis and evaluation of artificial neural networks (ANNs) when applied to replicate trajectories in molecular dynamics (MD) simulations or other particle methods. This study focuses on several architectures—feed
Externí odkaz:
https://doaj.org/article/48ed2c695d2544c8836567559b70fc8b
Autor:
Daw, Arka
Physics Informed Machine Learning (PIML) has emerged as the forefront of research in scientific machine learning with the key motivation of systematically coupling machine learning (ML) methods with prior domain knowledge often available in the form
Externí odkaz:
https://hdl.handle.net/10919/117966
Autor:
Alessio Alexiadis, Bahman Ghiassi
Publikováno v:
Results in Engineering, Vol 21, Iss , Pp 101721- (2024)
This micro-article introduces a method for integrating Large Language Models with geometry/mesh generation software and multiphysics solvers, aimed at streamlining physics-based simulations. Users provide simulation descriptions in natural language,
Externí odkaz:
https://doaj.org/article/6814351aff37432cbe6332ebe8177d84