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 ...

Nearly 200 years ago, the physicists Claude-Louis Navier and George Gabriel Stokes put the finishing touches on a set of equations that describe how fluids swirl. And for nearly 200 years, the ...

In this paper, linear quadratic optial control probles are solved by applying least square method based on Bézier control points. We divide the interval which includes t, into k subintervals and ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Population balance equation (PBE) models have the potential to automate many ...

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 ...

Engineers design safer cars, more resilient spacecraft, and stronger bridges using complex math problems that drive the underlying processes. Similarly, doctors use mathematical models to predict ...

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 ...

[Artificial Neural Networks for Solving Ordinary and Partial Differential Equations]https://www.cs.uoi.gr/~lagaris/papers/TNN-LLF.pdf), Lagaris etal, IEEE ...

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 ...