Optimal control of a coastal ecosystem through neural ordinary differential equations
Publication date
2024-10-06
Document type
Konferenzbeitrag
Author
Organisational unit
Conference
1st ECAI Workshop on “Machine Learning Meets Differential Equations: From Theory to Applications” (ML-DE Workshop at ECAI 2024) ; Santiago de Compostela, Spain ; October 20th, 2024
Publisher
MLResearchPress
Series or journal
Proceedings of machine learning research
ISSN
Periodical volume
255
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Abstract
Optimal control problems (OCPs) are essentials in various domains such as science, engineering, and industry, requiring the optimisation of control variables for dynamic systems, along with the corresponding state variables, that minimise a given performance index. Traditional methods for solving OCPs often rely on numerical techniques and can be computationally expensive when the discretisation grid or time horizon changes. In this work, we introduce a novel approach that leverages Neural Ordinary Differential Equations (Neural ODEs) to model the dynamics of control variables in OCPs. By embedding Neural ODEs within the optimisation problem, we effectively address the limitations of traditional methods, eliminating the need to re-solve the OCP under different discretisation schemes. We apply this method to a coastal ecosystem OCP, demonstrating its efficacy in solving the problem over a 50-year horizon and extending predictions up to 70 years without resolve the optimisation problem.
Version
Published version
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