The main objective of the journal MathematicS in Action is to promote the interactions of Mathematics with other scientific fields (Biology, Medicine, Economics, Computer Science, Physics, Chemistry, Mechanics, Environmental sciences, Engineering sciences, etc.) by publishing articles at their interfaces. These articles must be useful and globally accessible to both communities. Thus, the journal favours articles written by ay least two authors, one of them being a mathematician, the other one belonging to another scientific community.
The papers should address modelling issues (conception, analysis and validation of models), numerical and/or experimental methods.
They should preferably include both a mathematical part and, at choice, numerical or experimental results.
They should be very pedagogical on the motivations and the expected impact in both disciplines.
Each submitted paper will be evaluated equally for its mathematical quality and for its interest to the concerned application field. To be accepted a paper needs to be of the highest scientific quality, original, and strongly interdisciplinary.
The Journal is an electronic publication and all articles are freely available. However, each year a printed version is sent to a selection of libraries.
This journal, previously hosted by Cedram, is now web-published by the Centre Mersenne.
In 2019, Cedram has become the Centre Mersenne for open scientific publishing, a publishing platform for scientific journals developed by Mathdoc.
We describe an algorithm for the solution of a statistical/average atom non-local-thermodynamic-equilibrium atomic kinetics model of steady-state plasmas in which all one- and two-electron processes are included in full generality.
A new method for stochastic control based on neural networks and using randomisation of discrete random variables is proposed and applied to optimal stopping time problems. The method models directly the policy and does not need the derivation of a dynamic programming principle nor a backward stochastic differential equation. Unlike continuous optimization where automatic differentiation is used directly, we propose a likelihood ratio method for gradient computation. Numerical tests are done on the pricing of American and swing options. The proposed algorithm succeeds in pricing high dimensional American and swing options in a reasonable computation time, which is not possible with classical algorithms.
