New AI framework can uncover space physics equations in raw data

Artificial intelligence (AI) systems, particularly artificial neural networks, have proved to be highly promising tools for ...
The Hindu

Abel laureate Masaki Kashiwara changed how algebra meets analysis

In March this year, the Japanese mathematician Masaki Kashiwara found out over a Zoom call that he had been awarded the Abel Prize, one of mathematics’ highest honours, for “his fundamental ...
Phys.org

AI techniques excel at solving complex equations in physics, especially inverse problems

Differential equations are fundamental tools in physics: they are used to describe phenomena ranging from fluid dynamics to general relativity. But when these equations become stiff (i.e. they involve ...
Hosted on MSN

Solving a System of Linear Equations with a Matrix and Python

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...
IEEE

Federated Scientific Machine Learning for Approximating Functions and Solving Differential Equations With Data Heterogeneity

Abstract: By leveraging neural networks, the emerging field of scientific machine learning (SciML) offers novel approaches to address complex problems governed by partial differential equations (PDEs) ...
GitHub

Toolbox to solve infinite horizon DSGE models with time iteration and spline approximation.

Currently (6.11.24) the toolbox is only suited for upto 4-dimensional state space (although only tested upto 2D, but should be easily expanded to more than 4 dimensions in "grid_fun"), and a ...
IEEE

Fast Solving Partial Differential Equations via Imitative Fourier Neural Operator

Abstract: Neural operators are a class of neural networks to learn mappings between infinite-dimensional function spaces, and recent studies have shown that using neural operators to solve partial ...