Abstract: In this paper, we have developed an artificial neural network (ANN) method for finding solutions to the Dirichlet problem for fractional order differential equations (FODEs) 0 α1 using the ...

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A large class of problems leading to digital computer processing can be formulated in terms of the numerical solution of systems of ordinary differential equations. Powerful methods are in existence ...

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 ...

A decade ago, the city — and all of Michigan — had too many houses. Now it has a shortage. The shift there explains today’s costly housing market in the rest of the country. By Conor Dougherty ...

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 ...

Can you chip in? This year we’ve reached an extraordinary milestone: 1 trillion web pages preserved on the Wayback Machine. This makes us the largest public repository of internet history ever ...

This paper presents In-Context Operator Networks (ICON), a neural network approach that can learn new operators from prompted data during the inference stage without requiring any weight updates.

In this paper, the boundary value problems for second order singularly perturbed delay differential equations are treated. A generic numerical approach based on finite difference is presented to solve ...