Scientists are blending physics-informed AI with supercomputing to model plasma turbulence more accurately and efficiently. These breakthroughs could improve predictions for fusion reactors, ...

Python’s rich ecosystem of libraries like NumPy and SciPy makes it easier than ever to work with vectors, matrices, and linear systems. Whether you’re calculating determinants, solving equations, or ...

Nonlinear relationships are common in applied research, especially in education, health, and economics. While Python provides statsmodels for mixed-effects models and patsy for spline construction, ...

The era of size inclusivity is seemingly over. Our critic traces the shift and hopes designers might learn from it. By Vanessa Friedman I know models have always been skinny, but it seems to me they ...

In this work we present two main contributions: the first one is a Python implementation of the discrete approximation of the Laplace-Beltrami operator (LBO) (Belkin et al., 2008) allowing us to solve ...

Microsoft has added official Python support to Aspire 13, expanding the platform beyond .NET and JavaScript for building and running distributed apps. Documented today in a Microsoft DevBlogs post, ...

Physical modeling synthesis aims to simulate sound by solving the equations governing an instrument or acoustic system’s behaviour. This paradigm stands apart from signal-based methods by directly ...

We present a novel integrated mathematical and numerical framework for the nonlinear Schrödinger equation in open quantum systems under electromagnetic fields, with a particular focus on Bose-Einstein ...

Abstract: This paper presents a novel approach to practical nonlinear model predictive control (PNMPC) using Kolmogorov–Arnold networks (KANs) as prediction models. KANs are based on the ...