Mathematical models of natural processes can be used as inversion tools to predict unobserved properties from measured quantities. Uncertainty in observations and model formulation impact on the efficacy of inverse modelling. We present a general methodology, history matching, that can be used to investigate the effect of observational and model uncertainty on inverse modelling studies. We demo...

EDITORIAL article Front. Mater., 14 December 2022Sec. Computational Materials Science Volume 9 - 2022 | https://doi.org/10.3389/fmats.2022.1111499

One of the biggest experimental programs helping to improve nuclear installation safety is PHEBUS, an international cooperative research program that provides data for validating computer codes dedicated analysis severe accidents and their calculation results. In European Union, HORIZON 2020 EURATOM project MUSA (Management Uncertainties Severe Accidents) in progress. Modelling PHEBUS FPT-1 tes...

Data-driven turbulence modelling approaches are gaining increasing interest from the CFD community. However, introduction of a machine learning (ML) model introduces new source uncertainty, ML itself. Quantification this uncertainty is essential since predictive capability data-driven diminishes when predicting physics not seen during training. In work, we explore suitability Mondrian forests (...

Abstract This paper deals with the theoretical analysis of spatial concentration and localization firms employees over a set regions. In particular, it provides simple site-selection model to describe probabilistic framework location patterns. The adopted quantitative tool is stochastic theory urns. moves from empirical evidence deviation companies uniform distribution firms. Factors leading su...