Use cases

Below are some expected surmise use cases that we support (or are working to support) and plan to have examples of:

  • A user wants to emulate a computationally expensive model at a limited number of points in parameter space and interpolate those results through the whole space. Then, a user can use one of the methods in the \emulationmethods directory.

  • A user wants to emulate a computationally expensive model at a limited number of points in parameter space and interpolate those results through the whole space using a user-developed emulation method. Then, a user can drop the script of the method into the \emulationmethods directory, and use it within the surmise framework.

  • A user wants to perform principled uncertainty quantification that calibrates the models against data. Then, a user can use one of the methods in the \calibrationmethods directory.

  • A user wants to perform principled uncertainty quantification that calibrates the models against data via a user-developed calibration method. Then, a user can drop the script of the method into the \calibrationmethods directory, and use it within the surmise framework.

  • A user wants to test a new sampler within the surmise framework. Then, a user can drop the script of the method into the \utilitiesmethods directory, and use it within the surmise framework.

Reference to examples

Below is a non-exhaustive list of references utilizing surmise (in alphabetical order). If you wish to include your work in the list, please create an issue on Github issues. If you are viewing the manual pdf, please refer to the Bibliography section.

[1]

Andrius Burnelis, Vojta Kejzlar, and Daniel R. Phillips. Variational inference of effective range parameters for ³He-$^4$He scattering. arXiv preprint arXiv:2408.13250, 2024. URL: https://arxiv.org/abs/2408.13250.

[2]

Moses Y-H Chan, Matthew Plumlee, and Stefan M Wild. Constructing a simulation surrogate with partially observed output. Technometrics, 66(1):1–13, 2024. doi:10.1080/00401706.2023.2210170.

[3]

Pablo Giuliani, Kyle Godbey, Edgard Bonilla, Frederi Viens, and Jorge Piekarewicz. Bayes goes fast: uncertainty quantification for a covariant energy density functional emulated by the reduced basis method. Frontiers in Physics, 10:1212, 2023. doi:10.3389/fphy.2022.1054524.

[4]

Stephen Hudson, Jeffrey Larson, John-Luke Navarro, and Stefan M. Wild. libEnsemble: a library to coordinate the concurrent evaluation of dynamic ensembles of calculations. IEEE Transactions on Parallel and Distributed Systems, 33(4):977–988, 2022. doi:10.1109/tpds.2021.3082815.

[5]

Syed Afrid Jahan, Hendrik Roch, and Chun Shen. Bayesian analysis of (3+1)d relativistic nuclear dynamics with the RHIC beam energy scan data. arXiv preprint arXiv:2408.00537, 2024. URL: https://arxiv.org/abs/2408.00537.

[6]

Dananjaya Liyanage, Özge Sürer, Matthew Plumlee, Stefan M Wild, and Ulrich Heinz. Bayesian calibration of viscous anisotropic hydrodynamic simulations of heavy-ion collisions. Physical Review C, 108(5):054905, 2023. doi:10.1103/PhysRevC.108.054905.

[7]

Mengying Qiu, Bao-Jun Cai, Lie-Wen Chen, Cen-Xi Yuan, and Zhen Zhang. Bayesian model averaging for nuclear symmetry energy from effective proton-neutron chemical potential difference of neutron-rich nuclei. Physics Letters B, 849:138435, 2024. doi:10.1016/j.physletb.2023.138435.

[8]

Hendrik Roch, Syed Afrid Jahan, and Chun Shen. On model emulation and closure tests for 3+1d relativistic heavy-ion collisions. arXiv preprint arXiv:2405.12019, 2024. URL: https://arxiv.org/abs/2405.12019.

[9]

Özge Sürer, Filomena M Nunes, Matthew Plumlee, and Stefan M Wild. Uncertainty quantification in breakup reactions. Physical Review C, 106(2):024607, 2022. doi:10.1103/PhysRevC.106.024607.

[10]

Özge Sürer, Matthew Plumlee, and Stefan M Wild. Sequential bayesian experimental design for calibration of expensive simulation models. Technometrics, 66(2):157–171, 2024. doi:10.1080/00401706.2023.2246157.

[11]

Eugene Wickett, Matthew Plumlee, Karen Smilowitz, Souly Phanouvong, and Victor Pribluda. Inferring sources of substandard and falsified products in pharmaceutical supply chains. IISE Transactions, pages 1–16, 2023. doi:10.1080/24725854.2023.2174277.