# Uncertainty quantification for complex systems: theory and methodologies

Created: | 2018-01-10 15:31 |
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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 40 media items.

### Media items

#### 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

#### 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

#### 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

#### 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

#### Deterministic RBF Surrogate Methods for Uncertainty Quantification, Global Optimization and Parallel HPC Applications

Shoemaker, C

Thursday 8th February 2018 - 10:00 to 11:00

**Collection**:
Uncertainty quantification for complex systems: theory and methodologies

**Institution**:
Isaac Newton Institute for Mathematical Sciences

**Created**:
Fri 9 Feb 2018

#### Emulation for model discrepancy

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

#### Emulators for forecasting and UQ of natural hazards

Spiller, E

Wednesday 7th February 2018 - 10:00 to 11:00

**Collection**:
Uncertainty quantification for complex systems: theory and methodologies

**Institution**:
Isaac Newton Institute for Mathematical Sciences

**Created**:
Wed 7 Feb 2018

#### Experimental design in computer experiments: review and recent research

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

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

#### Group covariance functions for Gaussian process metamodels with categorical inputs

Roustant, O

Friday 9th February 2018 - 09:00 to 10:00

**Collection**:
Uncertainty quantification for complex systems: theory and methodologies

**Institution**:
Isaac Newton Institute for Mathematical Sciences

**Created**:
Mon 12 Feb 2018

#### High-Dimensional Collocation for Lognormal Diffusion Problems

Ernst, O

Friday 9th February 2018 - 11:30 to 12:30

**Collection**:
Uncertainty quantification for complex systems: theory and methodologies

**Institution**:
Isaac Newton Institute for Mathematical Sciences

**Created**:
Mon 12 Feb 2018

#### Linear Algebra Methods for Parameter-Dependent Partial Differential Equations

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

#### Modelling discontinuities in simulator output using Voronoi tessellations

Gosling, J

Tuesday 6th February 2018 - 13:30 to 14:30

**Collection**:
Uncertainty quantification for complex systems: theory and methodologies

**Institution**:
Isaac Newton Institute for Mathematical Sciences

**Created**:
Wed 7 Feb 2018

#### Multi-Index Stochastic Collocation (MISC) for Elliptic PDEs with random data

Tamellini, L

Tuesday 6th February 2018 - 11:30 to 12:30

**Collection**:
Uncertainty quantification for complex systems: theory and methodologies

**Institution**:
Isaac Newton Institute for Mathematical Sciences

**Created**:
Wed 7 Feb 2018

#### Multilevel Monte Carlo Methods

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

#### Nonstationary Gaussian process emulators with covariance mixtures

Williamson, D

Friday 9th February 2018 - 10:00 to 11:00

**Collection**:
Uncertainty quantification for complex systems: theory and methodologies

**Institution**:
Isaac Newton Institute for Mathematical Sciences

**Created**:
Mon 12 Feb 2018

#### Philosophical Approaches to Uncertainty and its Measurement

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