Presenters-0449: Difference between revisions
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|CSDMS meeting email address=jcaers@stanford.edu | |CSDMS meeting email address=jcaers@stanford.edu | ||
|CSDMS meeting title presentation= | |CSDMS meeting title presentation=Bayesian Evidential Learning: a protocol for uncertainty quantification in Earth systems | ||
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{{Presenters presentation | {{Presenters presentation | ||
|CSDMS meeting abstract presentation= | |CSDMS meeting abstract presentation=In this webinar, I will present a new framework termed “Bayesian Evidential Learning” (BEL) that streamlines the integration of these four components common to building Earth systems: data, model, prediction, decision. This idea is published in a new book: “Quantifying Uncertainty in Subsurface Systems” (Wiley-Blackwell, 2018) and applied to five real case studies in oil/gas, groundwater, contaminant remediation and geothermal energy. BEL is not a method, but a protocol based on Bayesianism that lead to the selection of relevant methods to solve complex modeling and decision problems. In that sense BEL, focuses on purpose-driven data collection and model-building. One of the important contributions of BEL is that is a data-scientific approach that circumvents complex inversion modeling relies on machine learning from Monte Carlo with falsified priors. The case studies illustrate how modeling time can be reduced from months to days, making it practical for large scale implementations. In this talk, I will provide an overview of BEL, how it relies on global sensitivity analysis, Monte Carlo, model falsification, prior elicitation and data scientific methods to implement the stated principle of its Bayesian philosophy. I will cover an extensive case study involving the managing of the groundwater system in Denmark. | ||
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Revision as of 11:54, 1 October 2019
CSDMS 2019 Webinars
Bayesian Evidential Learning: a protocol for uncertainty quantification in Earth systems
Abstract
In this webinar, I will present a new framework termed “Bayesian Evidential Learning” (BEL) that streamlines the integration of these four components common to building Earth systems: data, model, prediction, decision. This idea is published in a new book: “Quantifying Uncertainty in Subsurface Systems” (Wiley-Blackwell, 2018) and applied to five real case studies in oil/gas, groundwater, contaminant remediation and geothermal energy. BEL is not a method, but a protocol based on Bayesianism that lead to the selection of relevant methods to solve complex modeling and decision problems. In that sense BEL, focuses on purpose-driven data collection and model-building. One of the important contributions of BEL is that is a data-scientific approach that circumvents complex inversion modeling relies on machine learning from Monte Carlo with falsified priors. The case studies illustrate how modeling time can be reduced from months to days, making it practical for large scale implementations. In this talk, I will provide an overview of BEL, how it relies on global sensitivity analysis, Monte Carlo, model falsification, prior elicitation and data scientific methods to implement the stated principle of its Bayesian philosophy. I will cover an extensive case study involving the managing of the groundwater system in Denmark.
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Of interest for:
