Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...

The final, formatted version of the article will be published soon. Abstract—Globally, subtle hydrocarbon reservoirs in petrolifer ous basins have always been challenging targets for exploration r ...

Learn how to visualize a magnetic field model using Python! 🧲💻 In this tutorial, we’ll walk through creating a 2D vector field to represent the magnetic forces around a dipole. Perfect for physics ...

PythoC lets you use Python as a C code generator, but with more features and flexibility than Cython provides. Here’s a first look at the new C code generator for Python. Python and C share more than ...

Abstract: Although various approaches have been reported for forecasting aviation safety risks, they frequently fail to fully consider the stochastic nature and complex interrelations of numerous real ...

Official support for free-threaded Python, and free-threaded improvements Python’s free-threaded build promises true parallelism for threads in Python programs by removing the Global Interpreter Lock ...

A comprehensive Python library for calculating popular technical indicators used in financial markets. This repository provides efficient implementations of indicators like Supertrend, RSI, ADX, ...

This paper presents a novel approach to modeling stochastic systems using Dynamic Probabilistic Automata (DPA), which integrates deterministic and stochastic elements within a unified framework.

Quantification of risk metrics (VaR, ES, Loss Distribution, Hedging Error) via Monte Carlo simulation of stochastic models (GBM, Heston) with parameter estimation (MLE) on historical data.