Enhancements to the RAVEN code in FY16

Alfonsi, Andrea; Rabiti, Cristian; Maljovec, Dan; Mandelli, Diego; Smith, Curtis

doi:10.2172/1369372

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Automatic Selection Of High-fidelity and Surrogate Models: H1

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2017

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“…The HybridModel is designed to combine multiple surrogate models and any other Model (i.e. high-fidelity model) leveraging the EnsembleModel infrastructure developed in FY15 [ 9 ] and improvement last year [10], deciding which of the Model needs to be evaluated based on the model validation score.…”

Section: Automatic Selection Of High-fidelity and Surrogate Models: Hmentioning

confidence: 99%

Light Water Reactor Sustainability Program Status of Adaptive Surrogates within the RAVEN framework

Alfonsi

Wang

Cogliati

et al. 2017

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The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multipurpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN has demonstrated a good level of maturity in terms of state-of-art and advanced analysis methodologies. In the past year the RAVEN code has been released as open-source project and is available to download (raven.inl.gov) free of charge.

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Section: Automatic Selection Of High-fidelity and Surrogate Models: Hmentioning

confidence: 99%