Zobrazeno 1 - 10
of 796
pro vyhledávání: '"pinn"'
Publikováno v:
International Journal of Numerical Methods for Heat & Fluid Flow, 2024, Vol. 34, Issue 8, pp. 2963-2985.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/HFF-11-2023-0709
Autor:
Jihahm Yoo, Haesung Lee
Publikováno v:
AIMS Mathematics, Vol 9, Iss 10, Pp 27000-27027 (2024)
In this paper, we study physics-informed neural networks (PINN) to approximate solutions to one-dimensional boundary value problems for linear elliptic equations and establish robust error estimates of PINN regardless of the quantities of the coeffic
Externí odkaz:
https://doaj.org/article/97ba240a77c5468c9958ebc08a727f12
Autor:
Hart, William D.1 whart1@macalester.edu
Publikováno v:
Political Theology. May2020, Vol. 21 Issue 3, p269-273. 5p.
Autor:
White, Carol Wayne1 carol.white@bucknell.edu
Publikováno v:
Political Theology. May2020, Vol. 21 Issue 3, p256-261. 6p.
Autor:
Neal, Ronald B.1 rneal@wfu.edu
Publikováno v:
Black Theology: An International Journal. Nov2023, Vol. 21 Issue 3, p276-278. 3p.
Publikováno v:
Advances in Applied Energy, Vol 14, Iss , Pp 100167- (2024)
Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovolta
Externí odkaz:
https://doaj.org/article/e476e5be911845158b1afa60888a8999
Publikováno v:
Results in Physics, Vol 61, Iss , Pp 107716- (2024)
The Spin-Exchange Relaxation-Free (SERF) atomic magnetometers play an increasingly significant roles in cardiac and brain magnetometry fields, etc. For the SERF atomic magnetometer, the evolution and interaction with the magnetic field of atomic spin
Externí odkaz:
https://doaj.org/article/8d2e0d7058b24565ac20ccd105f689e5
Publikováno v:
Machine Learning with Applications, Vol 16, Iss , Pp 100563- (2024)
The application of deep neural networks towards solving problems in science and engineering has demonstrated encouraging results with the recent formulation of physics-informed neural networks (PINNs). Through the development of refined machine learn
Externí odkaz:
https://doaj.org/article/461764ec676244728a657c0d3c29034e
Publikováno v:
Case Studies in Thermal Engineering, Vol 56, Iss , Pp 104277- (2024)
In contemporary heat flow computations, the widespread application of deep learning, specifically Physical Informed Neural Networks (PINN), has been noted. However, existing PINN methods often exhibit limited applicability to specific operational con
Externí odkaz:
https://doaj.org/article/5a023744ef2542d18ca4353964c61515
Publikováno v:
IEEE Access, Vol 12, Pp 147753-147761 (2024)
Differential equations play a significant role in modeling of real world dynamical problems. A large amount of prior physical information in the form of differential equations are inherited in the dynamical systems. However, the black box machine lea
Externí odkaz:
https://doaj.org/article/6e585cd7e558486b853a379431ebe329