Explore non-trivial 1D square wells in Python with this detailed physics tutorial! 🐍⚛️ Learn how to model quantum systems, analyze energy levels, and visualize wave functions using Python simulations ...

Explore core physics concepts and graphing techniques in Python Physics Lesson 3! In this tutorial, we show you how to use Python to visualize physical phenomena, analyze data, and better understand ...

Physical AI is not merely a product feature. It is an architectural shift. The question before us is simple: Will the world of Physical AI be built by a few thousand engineers, or by millions of ...

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 ...

Learn how course data and instruction modes should be set up under Maintain Schedule of Classes in HUB. Please note: Multi-component courses can have different instruction modes for each section.

String manipulation is a core skill for every Python developer. Whether you’re working with CSV files, log entries, or text analytics, knowing how to split strings in Python makes your code cleaner ...

JSON Prompting is a technique for structuring instructions to AI models using the JavaScript Object Notation (JSON) format, making prompts clear, explicit, and machine-readable. Unlike traditional ...

Multiplication in Python may seem simple at first—just use the * operator—but it actually covers far more than just numbers. You can use * to multiply integers and floats, repeat strings and lists, or ...

This paper explores the integration of Artificial Intelligence (AI) large language models to empower the Python programming course for junior undergraduate students in the electronic information ...

In this tutorial, we delve into the creation of an intelligent Python-to-R code converter that integrates Google’s free Gemini API for validation and improvement suggestions. We start by defining the ...

Abstract: Deep neural networks (DNNs) have achieved satisfactory performance in multiple fields. However, recent studies have shown that DNNs can be easily fooled by adversarial examples. To mitigate ...