Hasil untuk "Physics"

M. Safronova, D. Budker, D. DeMille et al.

Advances in atomic physics, such as cooling and trapping of atoms and molecules and developments in frequency metrology, have added orders of magnitude to the precision of atom-based clocks and sensors. Applications extend beyond atomic physics and this article reviews using these new techniques to address important challenges in physics and to look for variations in the fundamental constants, search for interactions beyond the standard model of particle physics, and test the principles of general relativity.

S. Eberhart

T. Gaisser

P. Coleman

E. Esarey, C. Schroeder, W. Leemans

M. Raissi, P. Perdikaris, G. Karniadakis

We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this second part of our two-part treatise, we focus on the problem of data-driven discovery of partial differential equations. Depending on whether the available data is scattered in space-time or arranged in fixed temporal snapshots, we introduce two main classes of algorithms, namely continuous time and discrete time models. The effectiveness of our approach is demonstrated using a wide range of benchmark problems in mathematical physics, including conservation laws, incompressible fluid flow, and the propagation of nonlinear shallow-water waves.

A. Karpatne, William Watkins, J. Read et al.

This paper introduces a novel framework for learning data science models by using the scientific knowledge encoded in physics-based models. This framework, termed as physics-guided neural network (PGNN), leverages the output of physics-based model simulations along with observational features to generate predictions using a neural network architecture. Further, we present a novel class of learning objective for training neural networks, which ensures that the model predictions not only show lower errors on the training data but are also \emph{consistent} with the known physics. We illustrate the effectiveness of PGNN for the problem of lake temperature modeling, where physical relationships between the temperature, density, and depth of water are used in the learning of neural network model parameters. By using scientific knowledge to guide the construction and learning of neural networks, we are able to show that the proposed framework ensures better generalizability as well as physical consistency of results.

G. Cuniberti, G. Fagas, K. Richter et al.

H. Haber, G. Kane

D. Turcotte, G. Schubert

M. Salamon, P. Anderson, A. J. Cunningham et al.

D. Stauffer

G. Duvaut, J. Lions, C. John et al.

M. Ablikim, M. Achasov, P. Adlarson et al.

There has recently been a dramatic renewal of interest in hadron spectroscopy and charm physics. This renaissance has been driven in part by the discovery of a plethora of charmonium-like XYZ states at BESIII and B factories, and the observation of an intriguing proton-antiproton threshold enhancement and the possibly related X(1835) meson state at BESIII, as well as the threshold measurements of charm mesons and charm baryons. We present a detailed survey of the important topics in tau-charm physics and hadron physics that can be further explored at BESIII during the remaining operation period of BEPCII. This survey will help in the optimization of the data-taking plan over the coming years, and provides physics motivation for the possible upgrade of BEPCII to higher luminosity.

D. Agterberg, J. C. Davis, S. Edkins et al.

We review the physics of pair-density wave (PDW) superconductors. We begin with a macroscopic description that emphasizes order induced by PDW states, such as charge-density wave, and discuss related vestigial states that emerge as a consequence of partial melting of the PDW order. We review and critically discuss the mounting experimental evidence for such PDW order in the cuprate superconductors, the status of the theoretical microscopic description of such order, and the current debate on whether the PDW is a mother order or another competing order in the cuprates. In addition, we give an overview of the weak coupling version of PDW order, Fulde–Ferrell–Larkin–Ovchinnikov states, in the context of cold atom systems, unconventional superconductors, and noncentrosymmetric and Weyl materials.

V. Uversky

Recognition of the natural abundance and functional importance of intrinsically disordered proteins (IDPs) and hybrid proteins containing ordered and intrinsically disordered protein regions (IDPRs) is changing protein science. IDPs and IDPRs; i.e., functional proteins and protein regions without unique structures, are commonly found in all organisms, where they have crucial roles in various biological processes. Disorder-based functionality complements functions of ordered proteins and domains. However, by virtue of their existence, IDPs/IDPRs, which are characterized by the remarkable conformational flexibility and structural plasticity, break multiple rules elaborated over the years to explain structure, folding, and functionality of well-folded proteins with unique structures. Despite the general believe, which dominated in protein science for more than a century, that unique biological functions of proteins require unique 3D-structures, structure-less IDPs/IDPRs are functional, being able to perform impossible tricks and to be engaged in biological activities, which are improbable for ordered proteins. With their exceptional spatio-temporal heterogeneity and high conformational flexibility, IDPs/IDPRs represent complex systems that act at the edge of chaos and are specifically tunable by various means. In this article, some of the wanders of intrinsic disorder are discussed as illustrations of their ‘mysterious’ (meta)physics.

M. Le Gallo, A. Sebastian

Phase-change memory (PCM) is an emerging non-volatile memory technology that has recently been commercialized as storage-class memory in a computer system. PCM is also being explored for non-von Neumann computing such as in-memory computing and neuromorphic computing. Although the device physics related to the operation of PCM have been widely studied since its discovery in the 1960s, there are still several open questions relating to their electrical, thermal, and structural dynamics. In this article, we provide an overview of the current understanding of the main PCM device physics that underlie the read and write operations. We present both experimental characterization of the various properties investigated in nanoscale PCM devices as well as physics-based modeling efforts. Finally, we provide an outlook on some remaining open questions and possible future research directions.

E. Barausse, E. Berti, T. Hertog et al.

In this paper, which is of programmatic rather than quantitative nature, we aim to further delineate and sharpen the future potential of the LISA mission in the area of fundamental physics. Given the very broad range of topics that might be relevant to LISA,we present here a sample of what we view as particularly promising fundamental physics directions. We organize these directions through a “science-first” approach that allows us to classify how LISA data can inform theoretical physics in a variety of areas. For each of these theoretical physics classes, we identify the sources that are currently expected to provide the principal contribution to our knowledge, and the areas that need further development. The classification presented here should not be thought of as cast in stone, but rather as a fluid framework that is amenable to change with the flow of new insights in theoretical physics.

G. Bihlmayer, P. Nöel, D. Vyalikh et al.

G. Polverini, B. Gregorcic

The paper aims to fulfil three main functions: (1) to serve as an introduction for the physics education community to the functioning of large language models (LLMs), (2) to present a series of illustrative examples demonstrating how prompt-engineering techniques can impact LLMs performance on conceptual physics tasks and (3) to discuss potential implications of the understanding of LLMs and prompt engineering for physics teaching and learning. We first summarise existing research on the performance of a popular LLM-based chatbot (ChatGPT) on physics tasks. We then give a basic account of how LLMs work, illustrate essential features of their functioning, and discuss their strengths and limitations. Equipped with this knowledge, we discuss some challenges with generating useful output with ChatGPT-4 in the context of introductory physics, paying special attention to conceptual questions and problems. We then provide a condensed overview of relevant literature on prompt engineering and demonstrate through illustrative examples how selected prompt-engineering techniques can be employed to improve ChatGPT-4’s output on conceptual introductory physics problems. Qualitatively studying these examples provides additional insights into ChatGPT’s functioning and its utility in physics problem-solving. Finally, we consider how insights from the paper can inform the use of LLMs in the teaching and learning of physics.