Uncertainty quantification for complex systems: theory and methodologies
| 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
This collection contains 92 media items.
Media items
MSG Design of Experiments Seminar Series: The war against bias: experimental design for big data
Wynn, H
Wednesday 20th June 2018 - 14:05 to 14:55
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 22 Jun 2018
Parameter inference, model error and the goals of calibration
Williamson, D
Wednesday 11th April 2018 - 11:30 to 12:00
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 11 Apr 2018
A Bayesian Composite Gaussian Process Model and its Application
Santner, T
Friday 8th June 2018 - 11:00 to 13:00
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 8 Jun 2018
A domain-decomposition-based model reduction method for convection-diffusion equations with random coefficients
Zhang, G
Tuesday 6th February 2018 - 14:30 to 15:30
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 7 Feb 2018
A few elements of numerical analysis for PDEs with random coefficients of lognormal type
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
A Triple Model Reduction for Data-Driven Large-Scale Inverse Problems in High Dimensional Parameter Spaces
Bui-Thanh, T
Monday 5th March 2018 - 14:45 to 15:30
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 14 Mar 2018
Active Subspace Techniques to Construct Surrogate Models for Complex Physical and Biological Models
Smith, R
Monday 5th February 2018 - 14:30 to 15:30
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 6 Feb 2018
Adaptive Stochastic Galerkin Finite Element Approximation for Elliptic PDEs with Random Coefficients
Powell, C
Monday 5th February 2018 - 11:30 to 12:30
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 6 Feb 2018
Balanced model order reduction for linear systems driven by Lévy noise
Freitag, M
Monday 18th June 2018 - 11:00 to 13:00
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 18 Jun 2018
Bayesian calibration, history matching and model discrepancy
Oakley, J
Thursday 12th April 2018 - 09:00 to 10:00
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 13 Apr 2018
Bayesian model calibration for generalized linear models: An application in radiation transport
Bingham, D
Thursday 12th April 2018 - 13:30 to 14:30
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 13 Apr 2018
Bayesian optimal design for Gaussian process model
Adamou, M
Thursday 8th February 2018 - 16:00 to 17:00
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 9 Feb 2018
Bayesian Probabilistic Numerical Methods
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
Bayesian quadrature, energy minimization and kernel herding for space filling design
Pronzato, L
Friday 13th April 2018 - 09:00 to 10:00
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 13 Apr 2018
Complexity Challenges in Uncertainty Quantification for Scientific and Engineering Applications.
Gattiker, J
Monday 5th March 2018 - 11:00 to 11:45
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 6 Mar 2018
Computational models of the heart: Why they are useful, and how they would benefit from UQ
Clayton, R (University of Sheffield)
Thursday 11th January 2018 - 16:00 to 17:00
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 15 Jan 2018
Computer model calibration with large nonstationary spatial outputs: application to the calibration of a climate model
Guillas, S
Friday 13th April 2018 - 10:00 to 10:30
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 13 Apr 2018
Conditional-Value-at-Risk Estimation with Reduced-Order Models
Kramer, B
Thursday 8th March 2018 - 14:00 to 14:45
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 9 Mar 2018
Consistency of stepwise uncertainty reduction strategies for Gaussian processes
Bachoc, F
Friday 1st June 2018 - 11:00 to 13:0
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 1 Jun 2018
Deep Gaussian Process Priors for Bayesian Inverse Problems
Teckentrup, A
Thursday 12th April 2018 - 11:30 to 12:00
Collection: Uncertainty quantification for complex systems: theory and methodologies
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 13 Apr 2018