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 ...
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 ...
Meta learning is an emerging research direction in machine learning. Roughly speaking, meta learning concerns learning how to learn, and focuses on the understanding and adaptation of the learning itself, instead of just completing a specific learning task. That is, a meta learner needs to ...
"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. ...
in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present Review, which discusses methods and applications for new physics searches in the context of terrestrial high-energy physics experiments, including the Large Hadron Collider, rare ...
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-perfor...
The primary research question was to determine what PINNs are and their associated benefits and drawbacks. The research also focused on the outputs from the CRUNCH research group in the Division of Applied Mathematics at Brown University and then on the (Physics–Informed Learning Machines for Multi...
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 ...
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, ...
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...