Visualize the magnetic field of parallel current-carrying wires using Python. This short shows how to model and plot the field pattern, explore direction and strength, and build intuition for ...

This project analyzes relationships between key supply chain metrics to identify bottlenecks, predict delivery issues, and optimize cost structures across supplier networks. Strong Negative ...

Abstract: This letter investigates the stationarity characteristics of vehicle-to-infrastructure (V2I) channels in a cell-free context, based on 5.89 GHz field measurements. The Correlation Matrix ...

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 ...

Abstract: This article introduces TBF-CM, a transformer-based beamforming framework that leverages correlation matrix data. The core contribution is a transformer-based neural network (TNN) designed ...

Department of Mechanical Engineering, Stanford University, Stanford, California 94305, United States Precourt Institute for Energy, Woods Institute for the Environment, and Doerr School of ...

A hybrid Kohn–Sham Density Functional Theory (KS-DFT) and 1-electron Reduced Density Matrix Functional Theory (1-RDMFT) has recently been developed to describe strongly correlated systems at ...

Iris Flower Classification By: SHUBHAM KUMAR University roll. No. -202401100300243 The code includes data loading, preprocessing, visualization, model training, evaluation, and saving results. Below ...

Monitoring and extracting trends from web content has become essential for market research, content creation, or staying ahead in your field. In this tutorial, we provide a practical guide to building ...

ABSTRACT: This article examines the effect of economic vulnerability on inclusive growth across 49 developing countries from 1991 to 2020, focusing on the mitigating role of agricultural structural ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.