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Optimal control of agent-based models via surrogate modeling.

Luis L Fonseca1, Lucas Böttcher1,2, Borna Mehrad1

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This study introduces an algorithm to solve optimal control problems for agent-based models (ABMs). The method uses surrogate ordinary differential equations (ODEs) to efficiently find control strategies for complex systems in life sciences.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Mathematical Modeling

Background:

  • Agent-based models (ABMs) are crucial tools in life sciences for simulating complex systems.
  • Optimal control problems are vital for applications like medical digital twins, but computationally challenging for ABMs.
  • Existing methods struggle with the complexity and dimensionality of ABMs.

Purpose of the Study:

  • To develop and validate a novel algorithm for solving optimal control problems in agent-based models.
  • To enable efficient computation of control strategies for complex, high-dimensional ABMs.
  • To bridge the gap between ABM simulations and practical control applications in life sciences.

Main Methods:

  • The algorithm derives a lower-dimensional surrogate model, typically a system of ordinary differential equations (ODEs), from the original ABM.
  • It solves the optimal control problem using the derived ODE surrogate model.
  • The obtained control solution is then transferred back to the original ABM.

Main Results:

  • The algorithm successfully solves optimal control problems for general agent-based models.
  • Validation demonstrates the accuracy and efficiency of the ODE surrogate approach.
  • The method offers flexibility in ODE structure based on available ABM information.

Conclusions:

  • This algorithm provides a computationally efficient and versatile solution for optimal control in agent-based models.
  • It has broad applicability in life sciences, including ecology, epidemiology, and biomedicine.
  • The approach facilitates advanced modeling, such as for medical digital twin development.