Abstract: Many problems in science and engineering can be mathematically modeled using partial differential equations (PDEs), which are essential for fields like computational fluid dynamics (CFD), ...

TensorFlow implementation for DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations. Physics-informed neural networks are a type of promising tools to ...

Solving partial differential equations (PDEs) is a required step in the simulation of natural and engineering systems. The associated computational costs significantly increase when exploring various ...

SIAM Journal on Numerical Analysis, Vol. 50, No. 6 (2012), pp. 3351-3374 (24 pages) In this paper quasi-Monte Carlo (QMC) methods are applied to a class of elliptic partial differential equations ...

Abstract: We propose a numerical integration accelerator (INTIACC) that speeds up the solution of partial differential equations (PDEs) for scientific computing. In contrast to recent works, INTIACC ...

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Ask the publishers to restore access to 500,000+ books. An icon used to represent a menu that can be toggled by interacting with this icon. A line drawing of the Internet Archive headquarters building ...

A child prodigy born in Australia, Tao, 50, is now at the top of his field at the University of California at Los Angeles, working in the rarefied realms of partial differential equations or harmonic ...

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Tipping points in our climate predictions are both wildly dramatic and wildly uncertain. Can mathematicians make them useful?