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Artificial brains could point the way to ultra-efficient supercomputers
Sandia National Labs cajole Intel's neurochips into solving partial differential equations New research from Sandia National ...
A new technical paper titled “Solving sparse finite element problems on neuromorphic hardware” was published by researchers ...
Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.
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 ...
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 ...
Electromagnetic waves help to solve partial differential equations at the speed of light. Credit: R.G. MacDonald, A. Yakovlev, and V. Pacheco-Peña, doi 10.1117/1.APN.3.5.056007 In the domains of ...
Abstract: Solving the coupled partial differential equations (PDEs) that govern the dynamics of multiphysics systems is both important and challenging. Existing numerical methods such as the finite ...
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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