Uncertainty quantification for complex systems: theory and methodologies

Uncertainty quantification for complex systems: theory and methodologies's image
Created: 2018-01-10 15:31
Institution: Isaac Newton Institute for Mathematical Sciences
Description: In areas as diverse as climate modelling, manufacturing, energy, life sciences, finance, geosciences and medicine, mathematical models and their discretisations into computer models are routinely used to inform decisions, assess risk and formulate policies. How accurate are the predictions made using such models? This crucial question lies at the heart of uncertainty quantification (UQ).

UQ is a broad phrase used to describe methodologies for taking account of uncertainties when mathematical and computer models are used to describe real-world phenomena. This includes propagating uncertainty from unknown model inputs to model outputs, the study of uncertainty in the models themselves, developing approximation schemes that result in tractable and accurate computer models, robust design, model calibration and other inverse problems, model bias and discrepancy etc. This programme focuses on UQ for complex systems which have complicated mathematical descriptions such as systems of partial differential equations for which even a single deterministic inversion of an associated computer model is very costly.

The scientific challenges of modern life, the recent rapid growth in computing power and the demand for more accurate and precise predictions in areas affecting improved infrastructures, public safety and economic well-being have spawned a recent surge in UQ activity. New UQ methodologies have and are continuing to be developed by statisticians and applied mathematicians independently.

The main aim of the programme is to bring applied mathematicians and statisticians together to formulate a common mathematical foundation for UQ and to establish long-lasting interactions that will lead to significant advances in UQ theory and methodologies for complex systems. Participants will work together to develop theories and methodologies for reducing the cost of model inversion, increasing the level of tractable complexity in modelling, and enabling efficient risk assessment and decision making. Five core themes of common interest to statisticians and applied mathematicians will provide the focus. These are:

Surrogate models
Multilevel, multi-scale, and multi-fidelity methods
Dimension reduction methods
Inverse UQ methods
Careful and fair comparisons
Website: https://www.newton.ac.uk/event/unq
 

Media items

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Media items

A few elements of numerical analysis for PDEs with random coefficients of lognormal type

   13 views

Charrier, J (Aix Marseille Université)
Wednesday 10th January 2018 - 09:00 to 10:00

Collection: Uncertainty quantification for complex systems: theory and methodologies

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Mon 15 Jan 2018


Bayesian Probabilistic Numerical Methods

   14 views

Oates, C (Newcastle University)
Wednesday 10th January 2018 - 10:00 to 11:00

Collection: Uncertainty quantification for complex systems: theory and methodologies

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Mon 15 Jan 2018


Emulation for model discrepancy

   8 views

Goldstein, M
Monday 5th February 2018 - 13:30 to 14:30

Collection: Uncertainty quantification for complex systems: theory and methodologies

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Tue 6 Feb 2018


Experimental design in computer experiments: review and recent research

   19 views

Wynn, H (London School of Economics)
Tuesday 9th January 2018 - 11:30 to 12:30

Collection: Uncertainty quantification for complex systems: theory and methodologies

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Wed 10 Jan 2018


Gaussian process emulation

   22 views

O'Hagan, T (University of Sheffield)
Thursday 11th January 2018 - 10:00 to 11:00

Collection: Uncertainty quantification for complex systems: theory and methodologies

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Mon 15 Jan 2018


Linear Algebra Methods for Parameter-Dependent Partial Differential Equations

   11 views

Elman, H (University of Maryland)
Wednesday 10th January 2018 - 11:30 to 12:30

Collection: Uncertainty quantification for complex systems: theory and methodologies

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Mon 15 Jan 2018


Multilevel Monte Carlo Methods

   18 views

Scheichl, R (University of Bath)
Friday 12th January 2018 - 09:00 to 10:00

Collection: Uncertainty quantification for complex systems: theory and methodologies

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Mon 15 Jan 2018


Philosophical Approaches to Uncertainty and its Measurement

   22 views

Bradley, R (London School of Economics)
Monday 8th January 2018 - 16:30 to 17:30

Collection: Uncertainty quantification for complex systems: theory and methodologies

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Wed 10 Jan 2018


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