About This Course

Context

Numerical simulations have reached a great maturity that allows to accurately predict physics. However, uncertainties in the model inputs limit the capacities of deterministic simulations. Stochastic approaches allow to overcome this limit. Uncertainty Quantification (UQ) provides tools for propagating uncertainties through numerical simulators from inputs to output, quantifying the induced simulator output variability, screening the non-significant inputs, ranking the significant ones or even estimating an exceedance probability.

Scientific content

This online training course presents the fundamental concepts of uncertainty quantification. It is divided in 3 consecutive weeks:

• week 1: introduction to uncertainty quantification
• week 2: uncertainty propagation and sensitivity analysis
• week 3: formulation, validation and application of surrogate model

An interactive live conference will close the 3-week session, focussing on an Uncertainty Quantification analysis for an applicative test case in Computational Fluid Dynamics. The time slot for this conference will be decided regarding the attendees' availability in April.

Learning outcomes

At the end of this training, you will be able to:

• give a grounded opinion about various tools of UQ: sensitivty analysis, design of experiments, surrogate model,
• set up a UQ analysis on a simple problem: choose an appropriate set of tools for your case study,
• make the most of a UQ study (modelling the input uncertainties, sampling the uncertain input domain, quantifying the output uncertainty, analysing the output sensitivity with respect to the inputs...)

Our pedagogical principles

All our learning sessions are built upon evidence-based principles from cognitive psychology and learning research:

• concepts first: the course is focused on conceptual understanding of the meaning of equations and how they apply in practical cases (Van Heuvelen, 1991).
• active learning: the course is organized around activities especially designed to make participants interact between each other, involving a deep processing of the scientific content previously shown in short videos (Salmon, 2013).
• long-term retention and transfer: because you need to apply what you will learn during this session in the future and in various contexts, our courses are designed using the 10 laboratory-tested principles drawn from cognitive psychology (Halpern and Hakel, 2003).

Be prepared to be engaged and to interact with a community sharing a common goal: learning the scientific content of this course.

Prices

It costs 300 € for students, 350 € for CERFACS shareholders and 500 € for all others, including taxes. Once you have enrolled in the course, you will receive a link with information on how to pay. Credit cards are accepted via a Paypal transaction.