activity
20162022
most citedLearning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing

11 citations · 11 across the 1 of their papers we have counts for

collaborators

3 papers

math.NA2022★ 11 cited

Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing

Sebastian Becker, Arnulf Jentzen, Marvin S. Müller +1

In financial engineering, prices of financial products are computed approximately many times each trading day with (slightly) different parameters in each calculation. In many fina…

math.PR2017

Weak convergence rates for stochastic evolution equations and applications to nonlinear stochastic wave, HJMM, stochastic Schrödinger and linearized stochastic Korteweg-de Vries equations

Philipp Harms, Marvin S. Müller

We establish weak convergence rates for noise discretizations of a wide class of stochastic evolution equations with non-regularizing semigroups and additive or multiplicative nois…

math.PR2016

A stochastic Stefan-type problem under first-order boundary conditions

Marvin S. Mueller

Moving boundary problems allow to model systems with phase transition at an inner boundary. Driven by problems in economics and finance, in particular modeling of limit order books…