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Since 2019, Matheon's application-oriented mathematical research activities are being continued in the framework of the Cluster of Excellence MATH+
www.mathplus.de
The Matheon websites will not be updated anymore.

PD Dr. John Schoenmakers

Stellvertretender Leiter FG6, WIAS, PI ECMath project SE7 and SE22

Weierstrass Institut
Mohrenstrasse 39
10117 Berlin
+49 (0) 30 20372565
schoenma@wias-berlin.de


Research focus

Stochastic numerics
Stochastic control
Modeling in financial and energy markets

Projects as a project leader

  • SE7

    Optimizing strategies in energy and storage markets

    PD Dr. John Schoenmakers / Prof. Dr. Vladimir Spokoiny

    Project heads: PD Dr. John Schoenmakers / Prof. Dr. Vladimir Spokoiny
    Project members: Roland Hildebrand
    Duration: -
    Status: completed
    Located at: Weierstraß-Institut

    Description

    The project aims at developing numerical methods for the solution of complex optimal control problems arising in energy production, storage, and trading on energy markets. As a first step, we implement a Monte-Carlo approach to a hydro-electricity production and storage problem coupled with a stochastic model of the electricity market. Further we develop algorithms for pricing of complex energy derivatives based on the dual martingale approach.

    http://www.wias-berlin.de/projects/ECMath-SE7/
  • SE22

    Decisions in energy markets via deep learning and optimal control

    PD Dr. John Schoenmakers / Prof. Dr. Vladimir Spokoiny

    Project heads: PD Dr. John Schoenmakers / Prof. Dr. Vladimir Spokoiny
    Project members: Dr. Alexandra Suvorikova
    Duration: 01.06.2017 - 31.12.2018
    Status: completed
    Located at: Weierstraß-Institut

    Description

    One of the main goals in this project is a systematic numerical treatment of generic optimal decision problems in real-life applications that encounter in energy markets by incorporating ``deep Learning'', a recent concept for data analysis and prediction. Also it is intended to include principles of deep learning in methods for forecasting and estimating price distribution processes in a systematic way.

    https://www.wias-berlin.de/projects/ECMath-SE22/