Optimal Control of a Coastal Ecosystem Through Neural Ordinary Differential Equations

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Coelho, C (Author) Other; Costa, MFP (Author) Other; Ferrás, LL (Author) FEUP

Conference proceedings International

Title: 1ST ECAI WORKSHOP ON MACHINE LEARNING MEETS DIFFERENTIAL EQUATIONS: FROM THEORY TO APPLICATIONS

1st ECAI Workshop on Machine Learning Meets Differential Equations (ML-DE) - From Theory to Applications

Santiago de Compostela, SPAIN, OCT 20, 2024

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Other information

Authenticus ID: P-017-JAA

Abstract (EN): 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 re-solve the optimisation problem.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9