“I was curious to establish a baseline for when LLMs are effectively able to solve open math problems compared to where they ...

A $1 million prize awaits anyone who can show where the math of fluid flow breaks down. With specially trained AI systems, ...

If you are a math teacher in 2025, you have likely had The Conversation in the faculty lounge. It usually goes something like this:"I assigned a worksheet on qu ...

Abstract: In this study, physics-informed graph residual learning (PhiGRL) is proposed as an effective and robust deep learning (DL)-based approach for 3-D electromagnetic (EM) modeling. Extended from ...

NonlinearSystems.jl is a Julia package for solving nonlinear systems of equations and nonlinear least squares. It renovates well-trusted solution algorithms with highly performant and extensible ...

Abstract: Physics-Informed Neural Networks (PINNs) have recently emerged as a powerful method for solving differential equations by leveraging machine learning techniques. However, while neural ...