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 ...

A while back, [Chris Lu] was studying how analog circuits, specifically op-amps can be used to perform mathematical operations and wondered if they could be persuaded to solve differential equations, ...

We mentioned before about the \(+ c\) term. We are now going to look at how to find the value of \(c\) when additional information is given in the question.

We design a numerical scheme for solving a Dynamic Programming equation with Malliavin weights arising from the time-discretization of backward stochastic differential equations with the integration ...