@book{8377213604364f699ebbaab49b26d5f7,
title = "Report describing numerical improvement and developments and their application to treat uncertainty when dealing with coupled processes: Work Package 4: Development/improvement of numerical methods & tools for modelling coupled processes (DONUT) - Deliverable 4.7",
abstract = "This document is a compilation of the results of the work carried out in the framework of DONUT WP4. All participating institutions have contributed to this document by briefly describing their efforts to address uncertainties and sensitivity analysis. The short introduction of the efforts is presented here and the detailed reports are presented in the document chapters. SCK CEN's efforts cantered on accelerating reactive transport model (RTM) simulations using machine learning (ML), investigating the replacement of RTMs with ML methods for nonlinear regression. Their findings showed significant speedups of about 25-30 times against single-threaded RTM calculations, while maintaining high accuracy.",
keywords = "EURAD, DONUT",
author = "Eric Laloy and Diederik Jacques and Baksay Attila and Francis Claret",
note = "Score=1",
year = "2024",
month = may,
day = "16",
language = "English",
series = "EURAD Reports",
publisher = "EC - European Commission",
number = "D4.7",
address = "Belgium",
}