2024-10-06-banerjee24a.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
EMILY: Extracting sparse Model from ImpLicit dYnamics	Sparse model recovery requires us to extract model coefficients of ordinary differential equations (ODE) with few nonlinear terms from data. This problem has been effectively solved in recent literature for the case when all state variables of the ODE are measured. In practical deployments, measurements of all the state variables of the underlying ODE model of a process are not available, resulting in implicit (unmeasured) dynamics. In this paper, we propose EMILY, that can extract the underlying ODE of a dynamical process even if much of the dynamics is implicit. We show the utility of EMILY on four baseline examples and compare with the state-of-the-art techniques such as SINDY-MPC. Results show that unlike SINDY-MPC, EMILY can recover model coefficients accurately under implicit dynamics.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	banerjee24a	0	EMILY: Extracting sparse Model from ImpLicit dYnamics	1	11	1-11	1	false	Banerjee, Ayan and Gupta, Sandeep	given family Ayan Banerjee given family Sandeep Gupta	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/banerjee24a/banerjee24a.pdf