Abstract: In this paper, a novel approach leveraging artificial neural networks is introduced to approximate solutions for partial differential equations. The one ...

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: The simulation of seismic wave propagation through the solution of wave equations is considered one of the fundamental topics in applied geophysics. Physics-informed neural networks (PINNs) ...

This workshop provides comprehensive training in high-performance scientific computing using the Julia programming language and the SciML (Scientific Machine Learning) ecosystem. Designed for ...

This project explores the connection between Stochastic Gradient Descent (SGD), a central algorithm in deep learning, and the mathematical framework of Stochastic Differential Equations (SDEs).

Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we ...

Centre for Innovation and Standard for Medical Technology and Physical Therapy, Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand Department of Medical Technology ...