Hasil untuk "Physics"

Shengze Cai, Zhiping Mao, Zhicheng Wang et al.

Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is complex, and we cannot tackle high-dimensional problems governed by parametrized NSE. Moreover, solving inverse flow problems is often prohibitively expensive and requires complex and expensive formulations and new computer codes. Here, we review flow physics-informed learning, integrating seamlessly data and mathematical models, and implement them using physics-informed neural networks (PINNs). We demonstrate the effectiveness of PINNs for inverse problems related to three-dimensional wake flows, supersonic flows, and biomedical flows.

J. Rumble

Y. Tabe, K. Urayama, A. Matsuyama et al.

C. Bierlich, Smita Chakraborty, N. Desai et al.

This manual describes the Pythia event generator, the most recent version of an evolving physics tool used to answer fundamental questions in particle physics. The program is most often used to generate high-energy-physics collision “events”, i.e. sets of particles produced in association with the collision of two incoming high-energy particles, but has several uses beyond that. The guiding philosophy is to produce and re-produce properties of experimentally obtained collisions as accurately as possible. The program includes a wide ranges of reactions within and beyond the Standard Model, and extending to heavy ion physics. Emphasis is put on phenomena where strong interactions play a major role. The manual contains both pedagogical and practical components. All included physics models are described in enough detail to allow the user to obtain a cursory overview of used assumptions and approximations, enabling an informed evaluation of the program output. A number of the most central algorithms are described in enough detail that the main results of the program can be reproduced independently, allowing further development of existing models or the addition of new ones. Finally, a chapter dedicated fully to the user is included towards the end, providing pedagogical examples of standard use cases, and a detailed description of a number of external interfaces. The program code, the online manual, and the latest version of this print manual can be found on the Pythia web page: https://www.pythia.org/.

G. Cowan, K. Cranmer, E. Gross et al.

We describe likelihood-based statistical tests for use in high energy physics for the discovery of new phenomena and for construction of confidence intervals on model parameters. We focus on the properties of the test procedures that allow one to account for systematic uncertainties. Explicit formulae for the asymptotic distributions of test statistics are derived using results of Wilks and Wald. We motivate and justify the use of a representative data set, called the “Asimov data set”, which provides a simple method to obtain the median experimental sensitivity of a search or measurement as well as fluctuations about this expectation.

Y. Terletskii, H. Falk

Karen Barad

E. Yablonovitch

R. Hudman, N. E. Moore, R. V. Martin et al.

W. Ebeling

R. Feynman

Mark S. C. Reed, H. Simon

R. Armstrong, W. Paterson

R. Temam

T. Giamarchi

E. D. Cyan

R. Hilfer

Y. Zel’dovich, Y. Raizer, W. Hayes et al.

P. D. Gennes, Richard Alben