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Differentiable physics provides a new approach for modeling and understanding the physical systems by pairing the new technology of differentiable programming with classical numerical methods for physical simulation. We survey the rapidly…

Machine Learning · Computer Science 2021-09-17 Bharath Ramsundar , Dilip Krishnamurthy , Venkatasubramanian Viswanathan

Physics-informed neural networks (PINNs) have been popularized as a deep learning framework that can seamlessly synthesize observational data and partial differential equation (PDE) constraints. Their practical effectiveness however can be…

Machine Learning · Computer Science 2023-08-17 Sifan Wang , Shyam Sankaran , Hanwen Wang , Paris Perdikaris

We revisit the original approach of using deep learning and neural networks to solve differential equations by incorporating the knowledge of the equation. This is done by adding a dedicated term to the loss function during the optimization…

Machine Learning · Computer Science 2023-04-05 Hubert Baty , Leo Baty

Pairing plays an essential role in describing nuclear spectra and attempts to describe it has a long history in nuclear physics. Many theoretical tools were developed to treat the pairing problem either exactly or at various levels of…

Nuclear Theory · Physics 2019-01-30 A. B. Balantekin

This article discusses several erroneous claims which appear in textbooks on numerical methods and computational physics. These are not typos or mistakes an individual author has made, but widespread misconceptions. In an attempt to stop…

Physics Education · Physics 2020-12-25 Alexandros Gezerlis , Martin Williams

In this opening talk I will address some issues that exemplify the theoretical progress that has been made and is to be expected in the field of meson-nucleon physics. My emphasis will be on performing precision calculations to test aspects…

High Energy Physics - Phenomenology · Physics 2011-07-19 Ulf-G. Meißner

Today the applications of nuclear physics span a very broad range of topics and fields. This review discusses a number of aspects of these applications, including selected topics and concepts in nuclear reactor physics, nuclear fusion,…

Nuclear Theory · Physics 2017-01-18 Anna C. Hayes

A new approach that is a combination of classical thermodynamics and macroscopic kinetics is offered for studying the nucleation kinetics in condensed binary solutions. The theory covers the separation of liquid and solid solutions…

Chemical Physics · Physics 2018-11-14 Nikolay V. Alekseechkin

I present a grand view of some of the main particle physics challenges from a neutrino perspective. After a brief review of the current status of neutrino physics, I illustrate the possible role neutrinos can play in the elucidation of…

High Energy Physics - Phenomenology · Physics 2018-12-20 J. W. F. Valle

Personal view of author on goals and content of Mathematical Physics.

Mathematical Physics · Physics 2007-05-23 Ludwig Faddeev

For decades, physicists have used neutrinos from nuclear reactors to advance basic science. These pursuits have inspired many ideas for application of neutrino detectors in nuclear energy and security. While developments in neutrino…

The objective of this paper is to describe a development of an innovative approach to enable students studying science, technology, engineering, and mathematics (STEM) to apply the concepts learned in physics and mathematics to engineering…

Physics Education · Physics 2007-08-21 Roman Ya. Kezerashvili , Candido Cabo , Djafar K. Mynbaev

Learning advanced physics, in general, is challenging not only due to the increased mathematical sophistication but also because one must continue to build on all of the prior knowledge acquired at the introductory and intermediate levels.…

Physics Education · Physics 2016-02-18 Chandralekha Singh , Emily Marshman

This document is a hands-on, comprehensive guide to deep learning in the realm of physical simulations. Rather than just theory, we emphasize practical application: every concept is paired with interactive Jupyter notebooks to get you up…

Machine Learning · Computer Science 2025-03-28 N. Thuerey , B. Holzschuh , P. Holl , G. Kohl , M. Lino , Q. Liu , P. Schnell , F. Trost

Helping students become proficient problem-solvers is one of the primary goals of physics courses. In part 1 of this article, we summarized the vast research on problem-solving relevant for physics instruction, and here we discuss a…

Physics Education · Physics 2023-04-14 Chandralekha Singh , Alexandru Maries

Form a pure mathematical point of view, common functional forms representing different physical phenomena can be defined. For example, rates of chemical reactions, diffusion and heat transfer are all governed by exponential-type…

Machine Learning · Computer Science 2019-10-01 Navid Zobeiry , Keith D. Humfeld

A primary goal of physics is to create mathematical models that allow both predictions and explanations of physical phenomena. We weave maths extensively into our physics instruction beginning in high school, and the level and complexity of…

Physics Education · Physics 2016-04-12 Edward F. Redish

In this work, we present a teaching strategy implemented in Introduction to Physics, corresponding to the first year of the Physics Teacher Degree at the National University of Rosario, whose main purpose is to provide students with tools…

Physics Education · Physics 2025-02-20 Rodrigo E. Menchón , Santiago H. Luna , Andrea Fourty , Hugo D. Navone

This paper is a chapter from an upcoming book on cold molecule physics. In it we describe techniques for the preparation and manipulation of cold molecules. We further describe techniques for applying said cold molecules to tests of…

Atomic Physics · Physics 2009-05-18 M. R. Tarbutt , J. J. Hudson , B. E. Sauer , E. A. Hinds

Nuclear pairing correlations are known to play an important role in various single-particle and collective aspects of nuclear structure. After the first idea by A. Bohr, B. Mottelson and D. Pines on similarity of nuclear pairing to electron…

Nuclear Theory · Physics 2009-11-10 Vladimir Zelevinsky , Alexander Volya
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