ESAIM P&S Special Issue: SAMO 2025 - “Uncertainty Quantification and Sensitivity Analysis, from Theory to Applications”

Details

Published on 01 April 2025

• Céline Helbert
  Ecole Centrale de Lyon - Institut Camille Jordan – Université de Lyon, France.
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• Clémentine Prieur
  Laboratoire Jean Kuntzmann, AIRSEA Inria team, Université Grenoble Alpes, France.
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SCOPE

Sensitivity Analysis (SA) and Uncertainty Quantification (UQ) are highly cross-disciplinary fields. UQ is the process of characterizing all significant uncertainties in a model, simulation, or experiment and of quantifying their effect on computed or experimental results. SA in the context of UQ is the determination of how much uncertainty an individual source contributes to the total uncertainty in a simulated or experimental quantity. Both SA and UQ have established themselves as unifying tools within applied mathematics and computer science over the last few decades. They have created synergies between different fields such as statistics, probability, numerical analysis, machine learning and deep learning. They have also encouraged collaborations between mathematics and other disciplines such as biology, earth and environmental sciences, and engineering.

The Eleventh International Conference on Sensitivity Analysis of Model Output (SAMO) was held in Grenoble (France) in April, 2025. Since 1995, SAMO conferences are devoted to advances in research on sensitivity analysis methods and their interdisciplinary applications, bringing together researchers involved in the development and application of such methods. Following the 2025 conference but not restricted to SAMO 2025 participants, the aim of this special issue of ESAIM P&S journal is to present original /or theoretical ideas and advanced methodological developments in sensitivity analysis and uncertainty quantification connected subjects. Within this framework, topics of interest include (but are not limited to) active learning, complex data and/or models, conformal prediction, feature selection, kernel methods, optimal sampling, physics informed learning, reduced order modelling. Ideally, papers are expected to achieve a balance between academic material and practical applications.

April 28th, 2025: Announcement sent to potential contributors

September 30th, 2025: Paper submission deadline

Ongoing publication

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