BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Uncertainty Quantification and Stochastic Optimal Control: Navigat
 ing Complexity in Forecasting and Decision-Making
DTSTART:20250416T161500
DTSTAMP:20260521T033456Z
UID:42d8bb21889f933c74ddfb98e286edadff3f0700b9e5b4a990820798
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Raul Tempone\, KAUST - King Abdullah University Of Scien
 ce And Technology\, Arabie saoudite\nAbstract: In the era of data-driven d
 ecision-making\, accurately quantifying and managing uncertainty is crucia
 l across scientific and engineering disciplines. This talk introduces rece
 nt methodological advancements in Uncertainty Quantification (UQ) and Stoc
 hastic Optimal Control (SOC)\, highlighting novel computational techniques
  and their relevance in critical real-world scenarios.\n\nIn the first par
 t\, we discuss innovative UQ methods applied to complex systems\, modeling
  the forecast error using Stochastic Differential Equations (SDEs). We pre
 sent a data-driven approach that effectively quantifies uncertainties with
  concrete applications to renewable energy forecasting (wind and solar pho
 tovoltaic power). We also demonstrate a post-hoc UQ strategy for enhancing
  reliability in machine-learning-based biomedical image segmentation\, spe
 cifically for estimating left ventricle volumes in cardiac MRI.\n\nThe sec
 ond part of this talk focuses on recent methodological developments in SOC
 . Here\, we present a framework for continuous-time stochastic optimal con
 trol under discrete-time partial observations\, where control decisions in
 tegrate noisy measurements through a Bayesian update characterized by inte
 rlaced Hamilton–Jacobi–Bellman (HJB) equations with an infinite dimens
 ional state.
LOCATION:CM 1 517 https://plan.epfl.ch/?room==CM%201%20517
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR