When we think about heat traveling through a material, we typically picture diffusive transport, a process that transfers ...
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Unveiling the mysteries of computational fluid dynamics
Computational fluid dynamics (CFD) is a branch of physics that utilizes numerical methods and algorithms to analyze and predict the behavior of fluids and gases under various conditions. This field ...
PINN-based tools for detecting finite-time singularities in PDEs. Features lambda prediction formulas, funnel inference, and Gauss-Newton optimization. Independent implementation inspired by DeepMind ...
This repo contains the JAX implementation of our ICLR 2024 paper, Neural Spectral Methods: Self-supervised learning in the spectral domain. Yiheng Du, Nithin ...
Partial differential equations (PDEs) are workhorses of science and engineering. They describe a vast range of phenomena, from flow around a ship’s hull, to acoustics in a concert hall, to heat ...
ABSTRACT: This article is devoted to developing a deep learning method for the numerical solution of the partial differential equations (PDEs). Graph kernel neural networks (GKNN) approach to ...
A novel Haar scale-3 wavelet collocation technique is proposed in this study for dealing with a specific type of parabolic Buckmaster second-order non-linear partial differential equation in a ...
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Generic transport equations, comprising time-dependent partial differential equations (PDEs), delineate the evolution of extensive properties in physical systems, encompassing mass, momentum, and ...
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