No longer restricted to data analysis, machine learning is now increasingly being used in theory, experiment and simulation — a sign that data-intensive science is starting to encompass all traditional aspects of research.#Machine learning is no longer restricted to data analysis and is now ...
"When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning." arXiv preprint arXiv:2203.16797 (2022). ^Wang, Rui, and Rose Yu. "Physics-guided deep learning for dynamical systems: A survey." arXiv preprint arXiv:2107.01272 (2021). ^abCano, José-Ramón, et al. ...
Machine learning has been in use in high-energy particle physics for well over a decade, but the rise of deep learning in the early 2010s has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present ...
"This year's two Nobel Laureates in physics have used tools from physics to develop methods that are the foundation of today's powerful machine learning," the Nobel committee said in a press release. Hopfield's resea...
The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar...
A machine learning approach called physics-informed neural networks (PINNs), which can solve both forward and inverse problems of physical systems, was proposed and applied to the forward simulations of antiplane deformation. Here, we aimed to extend the PINN approach to crustal deformation in two...
Additionally, the application of machine learning in various contexts of nuclear physics research is demonstrated through several different cases, including the deep convolutional neural networks combined with theoretical models to extract information about the nuclear cluster structure from low level data, ...
Sam Raymond is a postdoctoral scholar at Stanford University, having completed his Ph.D. in the Center for Computational Science and Engineering (CCSE) at MIT. His research interests include physics-informed machine learning, applying high-performance...
Machine Learning Research AIP Publishing APL Machine Learning Resistance transient dynamics in switchable perovskite memristors Juan Bisquert; Agustín Bou; Antonio Guerrero; Enrique Hernández-Balaguera APL Mach. Learn. 1, 036101 (2023...
EPJ Data Science (2019) 8:33 https://doi.org/10.1140/epjds/s13688-019-0210-z REGULAR ARTICLE Open Access Mapping the physics research space: a machine learning approach Matteo Chinazzi1* , Bruno Gonçalves2, Qian Zhang1 and Alessandro Vespignani1 *Correspondence: m.chinazzi@northeastern....