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 ...

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 ...

Researchers from the Institute of Cosmos Sciences of the University of Barcelona (ICCUB) have developed a new framework based ...

Researchers from the Institute of Cosmos Sciences of the University of Barcelona (ICCUB) have developed a new framework based on machine learning ...

Learn how free IIT courses on SWAYAM are breaking barriers, offering quality education, and helping students and ...

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 ...

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) ...

Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning.

Jason Signor was preparing for a celebratory dinner in Manhattan as the final pieces came together for the sale of a large chunk of his company’s senior living business when everything went haywire.

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).