Abstract: Physics-informed neural networks (PINNs) offer a flexible framework for solving differential equations using physical constraints and data. This study focuses on second-order ...

Progress Check: We’re now about 23% of the way through the season, and Memorial Day – a day people say standings start to matter – is 18 days away. The point has come where we can’t simply dismiss 6 ...

Abstract: In this paper, we discuss the existence of oscillatory solutions for the following second order neutral delay differential equations\begin{equation*}{\left ...

Phenomena such as mechanical vibrations, resonance and oscillations can be mathematically described by second-order differential equation systems, commonly referred to as second-order systems. Working ...

In this paper, we establish the second-order differential equation system with the feedback controls for solving the problem of convex programming. Using Lagrange function and projection operator, the ...

I'm a physicist and I'm working with PINNs and other similar algorithms for the last three years to numerically solve differential equations. Some of these differential equations of my interest have ...

Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster. In high ...

Partial differential equations can describe everything from planetary motion to plate tectonics, but they’re notoriously hard to solve. Unless you’re a physicist or an engineer, there really isn’t ...

Differential equation is very important in science and engineering, because it required the description of some measurable quantities (position, temperature, population, concentration, electrical ...