{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:44:57Z","timestamp":1782841497958,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>We propose a sensitivity-driven framework for constructing Dynamic Bayesian Networks (DBNs) as approximations of Ordinary Differential Equations (ODEs) models while reducing the computational cost of generating training data. The approach uses global sensitivity rankings to identify the most influential direct and indirect dynamical dependencies, which are then used to define reduced sampling supports that capture essential system interactions without exhaustive simulations.\n\nThe methodology is evaluated on benchmark models of progressively higher dimensionality. A DBN built from a full training dataset serves as a reference and is compared with reduced constructions based on (i) equation sampling using only direct dependencies and (ii) sensitivity-driven supports incorporating both direct and selected indirect influences. This experimental setup allows us to assess whether equation-based sampling alone provides a sufficient approximation of the full model and to quantify the additional benefits of including indirect dynamical effects.\n\nResults show that the sensitivity-driven strategy drastically reduces the number of required simulations while maintaining the DBN\u2019s structural and probabilistic fidelity with respect to the original ODE dynamics.<\/jats:p>","DOI":"10.7148\/2026-0616","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:14Z","timestamp":1782838514000},"page":"616-623","source":"Crossref","is-referenced-by-count":0,"title":["A sensitivity-driven sampling reduction method for probabilistic approximations of odes"],"prefix":"10.7148","author":[{"given":"Olivier","family":"Bouet-Willaumez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrien Le Coent","family":"Le Coent","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benoit","family":"Barbot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nihal","family":"Pekergin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:22Z","timestamp":1782838522000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0616_simo_ecms2026_0059.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0616","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}