2024-10-06-coelho24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Optimal Control of a Coastal Ecosystem Through Neural Ordinary Differential Equations	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	coelho24a	0	Optimal Control of a Coastal Ecosystem Through Neural Ordinary Differential Equations	1	9	1-9	1	false	Coelho, Cec\'{i}lia and Costa, Fernanda and Ferr\'{a}s, Lu\'{i}s	given family Cecília Coelho given family Fernanda Costa given family Luís Ferrás	2024-10-06		Proceedings of the 1st ECAI Workshop on "Machine Learning Meets Differential Equations: From Theory to Applications"	255	inproceedings	date-parts 2024 10 6	https://raw.githubusercontent.com/mlresearch/v255/main/assets/coelho24a/coelho24a.pdf