Nature

Evaluating single multiplicative neuron models in physics-informed neural networks for differential equations

Artificial Neural Networks (ANNs), the cornerstone of modern machine learning techniques, are designed through mathematical modeling to emulate the functionality of biological neurons. Demonstrating ...
Nature

An improved data-free surrogate model for solving partial differential equations using deep neural networks

Partial differential equations (PDEs) are ubiquitous in natural science and engineering problems. Traditional discrete methods for solving PDEs are usually time-consuming and labor-intensive due to ...
CU Boulder News & Events

APPM 2360 Introduction to Differential Equations with Linear Algebra

Introduces ordinary differential equations, systems of linear equations, matrices, determinants, vector spaces, linear transformations, and systems of linear differential equations. Prereq., APPM 1360 ...
Scientific Research Publishing

Reproducing Kernel Hilbert Space Technique for Fourth-Order Singular Boundary Value Problems ()

Reproducing kernel Hilbert space method is utilized in this paper as an efficient approach to solve singular fourth order ...
JSTOR Daily

SIEVE ESTIMATION OF CONSTANT AND TIME-VARYING COEFFICIENTS IN NONLINEAR ORDINARY DIFFERENTIAL EQUATION MODELS BY CONSIDERING BOTH NUMERICAL ERROR AND ...

This is a preview. Log in through your library . Abstract This article considers estimation of constant and time-varying coefficients in nonlinear ordinary differential equation (ODE) models where ...