This study focuses on the numerical resolution of backward stochastic differential equations with data dependent on a jump-diffusion process. We propose and analyse a numerical scheme based on ...

MIT solved a century-old differential equation to break 'liquid' AI's computational bottleneck The discovery could usher in a new generation of weather forecasting and autonomous vehicle driving ...

We address a class of backward stochastic differential equations on a bounded interval, where the driving noise is a marked, or multivariate, point process. Assuming that the jump times are totally ...

Other neural nets haven’t progressed beyond simple addition and multiplication, but this one calculates integrals and solves differential equations.