Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Ordinary Differential Equations (ODEs) - Fixed-step and adaptive solvers with comprehensive features including event detection, dense output, and customizable and common recipes for solution output.

Abstract: In this article we use software Mat lab for verification solutions of geometric approach to the study of nonlinear singularly perturbed systems described by the second-order ordinary ...

Abstract: This paper introduces Physics-Informed Deep Equilibrium Models (PIDEQs) for solving initial value problems (IVPs) of ordinary differential equations (ODEs). Leveraging recent advancements in ...

Researchers have made a breakthrough in the ability to solve engineering problems. In a new paper published in Nature entitled, “A scalable framework for learning the geometry-dependent solution ...

Are you struggling to solve quadratic equations? Look no further than the “Almighty Formula,” a powerful mathematical tool that can crack even the toughest quadratic puzzles. In this article, you will ...

This project demonstrates the use of finite difference methods to solve Laplace's and Maxwell's equations using MATLAB. It includes a 2D solver for potential distribution and a 1D FDTD simulation for ...

Whether it's physical phenomena, share prices or climate models—many dynamic processes in our world can be described mathematically with the aid of partial differential equations. Thanks to ...

The paper aims to utilize an integral transform, specifically the Khalouta transform, an abstraction of various integral transforms, to address fractional differential equations using both ...