• Deutsch
Login

  • Home
  • Search
  • Browse
  • Publish
  • FAQ

Refine

Author

  • Djurdjevac Conrad, Natasa (2)
  • Djurdjevac, Ana (2)
  • Helfmann, Luzie (2)
  • Schütte, Christof (2)
  • Winkelmann, Stefanie (2)

Year of publication

  • 2019 (2) (remove)

Document Type

  • Article (1)
  • ZIB-Report (1)

Has Fulltext

  • no (1)
  • yes (1)

Institute

  • Computational Molecular Design (2)
  • Computational Systems biology (2)
  • Numerical Mathematics (2)

2 search hits

  • 1 to 2
  • BibTeX
  • CSV
  • RIS
  • XML
  • 10
  • 20
  • 50
  • 100

Sort by

  • Year
  • Year
  • Title
  • Title
  • Author
  • Author
From interacting agents to density-based modeling with stochastic PDEs (2019)
Helfmann, Luzie ; Djurdjevac Conrad, Natasa ; Djurdjevac, Ana ; Winkelmann, Stefanie ; Schütte, Christof
Many real-world processes can naturally be modeled as systems of interacting agents. However, the long-term simulation of such agent-based models is often intractable when the system becomes too large. In this paper, starting from a stochastic spatio-temporal agent-based model (ABM), we present a reduced model in terms of stochastic PDEs that describes the evolution of agent number densities for large populations. We discuss the algorithmic details of both approaches; regarding the SPDE model, we apply Finite Element discretization in space which not only ensures efficient simulation but also serves as a regularization of the SPDE. Illustrative examples for the spreading of an innovation among agents are given and used for comparing ABM and SPDE models.
From interacting agents to density-based modeling with stochastic PDEs (2019)
Helfmann, Luzie ; Djurdjevac Conrad, Natasa ; Djurdjevac, Ana ; Winkelmann, Stefanie ; Schütte, Christof
Many real-world processes can naturally be modeled as systems of interacting agents. However, the long-term simulation of such agent-based models is often intractable when the system becomes too large. In this paper, starting from a stochastic spatio-temporal agent-based model (ABM), we present a reduced model in terms of stochastic PDEs that describes the evolution of agent number densities for large populations. We discuss the algorithmic details of both approaches; regarding the SPDE model, we apply Finite Element discretization in space which not only ensures efficient simulation but also serves as a regularization of the SPDE. Illustrative examples for the spreading of an innovation among agents are given and used for comparing ABM and SPDE models.
  • 1 to 2

OPUS4 Logo

  • Contact
  • Impressum und Datenschutz
  • Sitelinks