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 ...

Abstract: In this work, we have developed a variational Bayesian inference theory of elasticity, which is accomplished by using a mixed Variational Bayesian inference Finite Element Method (VBI-FEM) ...

Freed from intelligibility and aesthetics, AI designs faster ...

Abstract: In this article, an unrolling algorithm of the iterative subspace-based optimization method (SOM) is proposed for solving full-wave inverse scattering problems (ISPs). The unrolling network, ...

️Flow matching is a recent state-of-the-art framework for generative modeling based on ordinary differential equations (ODEs). While closely related to diffusion models, it provides a more general ...

Clarkson University researchers have developed an artificial intelligence tool that can uncover the mathematical equations ...

Equations that have more than one unknown can have an infinite number of solutions. For example, \(2x + y = 10\) could be solved by: \(x = 1\) and \(y = 8\) \(x = 2\) and \(y = 6\) \(x = 3\) and \(y = ...

What’s the secret to prompting an AI to solve math problems that have left humans stumped? Tell it to believe in itself ...

How can you have a proof without proving anything? Mathematicians found a way and, in the process, came to blows over it – ...

This requires an algorithm: students are taught to stack one number atop another and multiply each digit of the bottom number ...

For more than a century, physicists debated which way a submerged sprinkler sucking in water would spin. Careful experiments ...

July 8, 2026 Researchers have created an AI-based simulation that makes it much faster to model how neutron star mergers produce many of the universe's heaviest elements. The new tool could improve ...