Discover how this powerful open-source SPICE simulator helps you analyse and validate analog, digital and mixed-signal circuits long before hardware is built with detailed netlist control, real ...
Abstract: Analytically solving complex or large-scale differential equations is often difficult or even impossible, making numerical integration methods indispensable. However, as all numerical ...
PyOdys is a robust and flexible Python package for solving ordinary differential equations (ODEs) of the form: $$ M(t, y)\frac{dy}{dt} = F(t, y). $$. Adaptive Time ...
Adequate mathematical modeling is the key to success for many real-world projects in engineering, medicine, and other applied areas. Once a well-suited model is established, it can be thoroughly ...
The development of machine learning (ML) models for scientific applications has long been hindered by the lack of suitable datasets that capture the complexity and diversity of physical systems. Many ...
Machine learning, particularly neural networks, has rapidly permeated most activities and work where data has a story to tell. Recently, deep learning has started to be used for solving differential ...
1 College of Mathematics and Information Science, Nanchang Hangkong University, Nanchang, China. 2 School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China.
Inspired by path integral solutions to the quantum relaxation problem, we develop a numerical method to solve classical stochastic differential equations with multiplicative noise that avoids ...
Abstract: Solving partial differential equations (PDEs) is a fundamental task for computational electromagnetic and mechanical wave modeling, which hold utmost significance in remote sensing and ...
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