A Deep Neural Network Algorithm for Semilinear Elliptic PDEs with Applications in Insurance Mathematics

Stefan Kremsner, Alexander Steinicke, Michaela Szölgyenyi

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1 Citation (Scopus)


In insurance mathematics, optimal control problems over an infinite time horizon arise when computing risk measures. An example of such a risk measure is the expected discounted future dividend payments. In models which take multiple economic factors into account, this problem is high-dimensional. The solutions to such control problems correspond to solutions of deterministic semilinear (degenerate) elliptic partial differential equations. In the present paper we propose a novel deep neural network algorithm for solving such partial differential equations in high dimensions in order to be able to compute the proposed risk measure in a complex high-dimensional economic environment. The method is based on the correspondence of elliptic partial differential equations to backward stochastic differential equations with unbounded random terminal time. In particular, backward stochastic differential equations—which can be identified with solutions of elliptic partial differential equations—are approximated by means of deep neural networks.

Original languageEnglish
Article number136
Pages (from-to)1-18
Number of pages18
Issue number4
Publication statusPublished - 9 Dec 2020


  • backward stochastic differential equations
  • semilinear elliptic partial differential equations
  • stochastic optimal control
  • unbounded random terminal time
  • machine learning
  • deep neural networks

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