Outputs (51)

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling (2022)
Journal Article
Swallow, B., Birrell, P., Blake, J., Burgman, M., Challenor, P., Coffeng, L. E., Dawid, P., De Angelis, D., Goldstein, M., Hemming, V., Marion, G., McKinley, T. J., Overton, C. E., Panovska-Griffiths, J., Pellis, L., Probert, W., Shea, K., Villela, D., & Vernon, I. (2022). Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling. Epidemics, 38, https://doi.org/10.1016/j.epidem.2022.100547

The estimation of parameters and model structure for informing infectious disease response has become a focal point of the recent pandemic. However, it has also highlighted a plethora of challenges remaining in the fast and robust extraction of infor... Read More about Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.

JUNE: open-source individual-based epidemiology simulation (2021)
Journal Article
Aylett-Bullock, J., Cuesta-Lazaro, C., Quera-Bofarull, A., Icaza-Lizaola, M., Sedgewick, A., Truong, H., Curran, A., Elliott, E., Caulfield, T., Fong, K., Vernon, I., Williams, J., Bower, R., & Krauss, F. (2021). JUNE: open-source individual-based epidemiology simulation. Royal Society Open Science, 8(7), https://doi.org/10.1098/rsos.210506

We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic i... Read More about JUNE: open-source individual-based epidemiology simulation.

A Bayesian Optimisation Workflow for Field Development Planning Under Geological Uncertainty (2020)
Presentation / Conference Contribution
Bordas, R., Heritage, J., Javed, M., Peacock, G., Taha, T., Ward, P., Vernon, I., & Hammersley, R. (2020, September). A Bayesian Optimisation Workflow for Field Development Planning Under Geological Uncertainty. Presented at ECMOR XVII

Field development planning using reservoir models is a key step in the field development process. Numerical optimisation of specific field development strategies is often used to aid planning. Bayesian Optimisation is a popular optimisation method th... Read More about A Bayesian Optimisation Workflow for Field Development Planning Under Geological Uncertainty.

A Bayesian Statistical Approach to Decision Support for TNO OLYMPUS Well Control Optimisation under Uncertainty (2020)
Presentation / Conference Contribution
Owen, J., Vernon, I., & Hammersley, R. (2020, September). A Bayesian Statistical Approach to Decision Support for TNO OLYMPUS Well Control Optimisation under Uncertainty. Presented at ECMOR XVII

Well control and field development optimisation are tasks of increasing importance within the petroleum industry, as seen by the development of and large participation in the 2018 TNO OLYMPUS Field Development Optimisation Challenge. Complex mathemat... Read More about A Bayesian Statistical Approach to Decision Support for TNO OLYMPUS Well Control Optimisation under Uncertainty.

Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques (2020)
Presentation / Conference Contribution
Formentin, H. N., Vernon, I., Goldstein, M., Caiado, C., Avansi, G., & Schiozer, D. (2020, September). Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques. Presented at ECMOR XVII

Model discrepancy specifies unavoidable differences between a physical system and its corresponding computer model. Incomplete information, simplifications and lack of knowledge about the physical state originate model discrepancy. Misevaluation of m... Read More about Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques.

Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis (2020)
Journal Article
Ferreira, C., Vernon, I., Caiado, C., Formentin, H., Avansi, G., Goldstein, M., & Schiozer, D. (2020). Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis. SPE Journal, 25(4), 2119-2142. https://doi.org/10.4043/29801-ms

When performing classic uncertainty reduction based on dynamic data, a large number of reservoir simulations need to be evaluated at high computational cost. As an alternative, we construct Bayesian emulators that mimic the dominant behaviour of the... Read More about Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis.

Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching (2020)
Journal Article
Jackson, S., Vernon, I., Liu, J., & Lindsey, K. (2020). Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching. Statistical Applications in Genetics and Molecular Biology, 19(2), Article 20180053. https://doi.org/10.1515/sagmb-2018-0053

A major challenge in plant developmental biology is to understand how plant growth is coordinated by interacting hormones and genes. To meet this challenge, it is important to not only use experimental data, but also formulate a mathematical model. F... Read More about Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching.

Gaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching process (2019)
Journal Article
Formentin, H. N., Almeida, F. L. R., Avansi, G. D., Maschio, C., Schiozer, D. J., Caiado, C., Vernon, I., & Goldstein, M. (2019). Gaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching process. Journal of Petroleum Science and Engineering, 173, 1080-1096. https://doi.org/10.1016/j.petrol.2018.10.045

History matching (HM) is an inverse problem where uncertainties in attributes are reduced by comparison with observed dynamic data. Typically, normalized misfit summarizes dissimilarities between observed and simulation data. Especially for long-time... Read More about Gaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching process.

Systematic uncertainty reduction for petroleum reservoirs combining reservoir simulation and Bayesian emulation techniques (2019)
Presentation / Conference Contribution
Nandi Formentin, H., Vernon, I., Avansi, G. D., Caiado, C., Maschio, C., Goldstein, M., & Schiozer, D. J. (2019, June). Systematic uncertainty reduction for petroleum reservoirs combining reservoir simulation and Bayesian emulation techniques. Presented at SPE Europec featured at 81st EAGE Annual Conference 2019, London

Reservoir simulation models incorporate physical laws and reservoir characteristics. They represent our understanding of sub-surface structures based on the available information. Emulators are statistical representations of simulation models, offeri... Read More about Systematic uncertainty reduction for petroleum reservoirs combining reservoir simulation and Bayesian emulation techniques.

Known Boundary Emulation of Complex Computer Models (2019)
Journal Article
Vernon, I., Jackson, S., & Cumming, J. (2019). Known Boundary Emulation of Complex Computer Models. SIAM/ASA Journal on Uncertainty Quantification, 7(3), 838-876. https://doi.org/10.1137/18m1164457

Computer models are now widely used across a range of scientific disciplines to describe various complex physical systems, however to perform full uncertainty quantification we often need to employ emulators. An emulator is a fast statistical constru... Read More about Known Boundary Emulation of Complex Computer Models.