A Python API for Dakota Mark Piper, Eric Hutton, and James Syvitski - - PowerPoint PPT Presentation

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Jun 29, 2023 173 likes •329 views

A Python API for Dakota Mark Piper, Eric Hutton, and James Syvitski CSDMS University of Colorado Boulder Agenda Uncertainty quantification Dakota The CSDMS Dakota Interface (a.k.a. Dakotathon) An experiment Summary

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A Python API for Dakota

Mark Piper, Eric Hutton, and James Syvitski CSDMS University of Colorado Boulder

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Agenda

Uncertainty quantification
Dakota
The CSDMS Dakota Interface (a.k.a. Dakotathon)
An experiment
Summary and future work

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(image courtesy J. Adam Stephens and Laura Swiler, SNL)

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dakotathon

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Given uncertain T and P, what’s the likelihood of the Waipaoa producing hyperpycnal plumes?

Mulder et al. (2003)

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An experiment

1000-yr Hydrotrend runs with defaults, except for T and P, which are

uniformly distributed about ±10% from default values, and L = 3.0

100 samples from T-P parameter space are selected using LHS
Count of daily output Cs > 40 kg m-3 is the response statistic
Use moments, correlations, PDF, and CDF to assess RI

https://github.com/mdpiper/AGU-2016

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RI = 8.4 ± 0.4 yr

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https://github.com/csdms/dakota

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Summary

Uncertainty quantification is vital for communicating model predictions to

policymakers and to the public

Dakota is powerful, but it requires user code to interface with a model
Dakotathon presents an easier-to-use Python interface

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Future work

Expose more Dakota analysis techniques
Incorporate Dakotathon into the CSDMS Web Modeling Tool, WMT
Perform a sensitivity study on Hydrotrend’s L

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Thank you!

Dakotathon Experiments Email GitHub, Twitter https://github.com/csdms/dakota https://github.com/mdpiper/AGU-2016 mark.piper@colorado.edu @mdpiper