Abstract: This letter solves the Sylvester equation in the form of $AX+XB=C $ in a distributed way, and proposes a distributed continuous-time algorithm when there is ...

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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 ...

Audrey Hendley chats with Newsweek about her decades-long career at American Express and how she proved that innovation could come from within.

The team has improved the capabilities of physics-informed neural networks (PINNs), a type of artificial intelligence that incorporates physical laws into the learning process. Researchers from the ...

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

Tracie Lee, a lecturer in the College of Business and Economics, has significantly expanded educational resources for students by recording 21 new Excel tutorial videos for McGraw Hill.

A mathematical brain teaser challenges people to solve the matchstick equation in just two moves. The puzzle uses matchsticks ...

Use these skills and tools to make the most of it. by Antonio Nieto-Rodriguez Quietly but powerfully, projects have displaced operations as the economic engine of our ...

Abstract: Nonlinear equations systems (NESs) are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots. Evolutionary algorithms (EAs) are one ...

This repository allows you to solve forward and inverse problems related to partial differential equations (PDEs) using finite basis physics-informed neural networks (FBPINNs). To improve the ...