– Campus Golm, Haus 9, Raum 1.10
Parameter Estimation from Noisy Observations of a Stochastic Heat Equation
Gregor Pasemann (HU)
Kim Aleksandra, ETH Zürich and Paul Scherrer Institute (Switzerland)
In today’s complex world and economy, international supply chains of goods cause global environmental impacts. Life Cycle Assessment (LCA) is a well established tool used for quantification of product impacts that arise throughout the entire chain. It aims at supporting environmentally informed decisions in policy-making, product development, and consumer choices. However, LCA results can only be interpreted confidently if the quality of the underlying data is satisfactory. To this end, Global Sensitivity Analysis (GSA) and Uncertainty Quantification are used to determine sources of uncertainties and thus, support prioritized data collection. Traditional GSA methods have only been applied to reduced LCA models with up to hundreds of inputs. In this work we apply GSA to comprehensive LCA models with hundreds of thousands of uncertain input datasets, and investigate notions of convergence, stability and validation of results.
***Due to the current situation concerning the pandemic the seminar will be held online. Please contact sebastian.reich[at]uni-potsdam.de to receive the zoom link.***