ArticlePLoS computational biology2025
Optimal control of agent-based models via surrogate modeling.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Learning dynamical systems with biochemically informed neural ordinary differential equations.bioRxiv : the preprint server for biology · 2026Article
- Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems.PLoS computational biology · 2026Article
- Format-Preserving Reduction of Canonical Nonlinear Models.Bulletin of mathematical biology · 2026Article
- Distinct mechanisms drive post-antibiotic Tuberculosis relapse post-cure versus post-treatment-failure.bioRxiv : the preprint server for biology · 2026Article
- Dynamical behavior analysis of 2-control strategies on tuberculosis model.PLOS global public health · 2026Article
- Advances in surrogate modeling for biological agent-based simulations: trends, challenges, and future prospects.Journal of mathematical biology · 2025Review
- A novel machine-learning based optimization: identifying new treatment regimens for tuberculosis.Numerical algebra, control and optimization · 2025Article
- Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations.PLoS computational biology · 2025Article
- Personalizing computational models to construct medical digital twins.Journal of the Royal Society, Interface · 2025Article
- Smart epidemic control: A hybrid model blending ODEs and agent-based simulations for optimal, real-world intervention planning.PLoS computational biology · 2025Article
- Control of medical digital twins with artificial neural networks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
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4 authors.
Funding
Abstract
This paper describes and validates an algorithm to solve optimal control problems for agent-based models (ABMs). For a given ABM and a given optimal control problem, the algorithm derives a surrogate model, typically lower-dimensional, in the form of a system of ordinary differential equations (ODEs), solves the control problem for the surrogate model, and then transfers the solution back to the original ABM. It applies to quite general ABMs and offers several options for the ODE structure, depending on what information about the ABM is to be used. There is a broad range of applications for such an algorithm, since ABMs are used widely in the life sciences, such as ecology, epidemiology, and biomedicine and healthcare, areas where optimal control is an important purpose for modeling, such as for medical digital twin technology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.