activity
20162022
most citedPrincipal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: application to urban drainage simulation

93 citations · 150 across the 5 of their papers we have counts for

collaborators

5 papers

physics.ao-ph2022

Measuring diameters and velocities of artificial raindrops with a neuromorphic dynamic vision sensor disdrometer

Jan Steiner, Kire Micev, Asude Aydin +2

Hydrometers that can measure size and velocity distributions of precipitation are needed for research and corrections of rainfall estimates from weather radars and microwave links.…

eess.SY2022★ 1 cited

Energy self-sufficient systems for monitoring sewer networks

Simon Mathis, Juan-Mario Gruber, Christian Ebi +3

Underground infrastructure networks form the backbone of vital supply and disposal systems. However, they are under-monitored in comparison to their value. This is due, in large pa…

stat.ME2019★ 9 cited

Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator

David Machac, Peter Reichert, Jörg Rieckermann +2

As in many fields of dynamic modeling, the long runtime of hydrological models hinders Bayesian inference of model parameters from data. By replacing a model with an approximation…

stat.CO2017★ 93 cited

Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: application to urban drainage simulation

Joseph B. Nagel, Jörg Rieckermann, Bruno Sudret

This paper presents an efficient surrogate modeling strategy for the uncertainty quantification and Bayesian calibration of a hydrological model. In particular, a process-based dyn…

stat.ME2016★ 47 cited

Appraisal of data-driven and mechanistic emulators of nonlinear hydrodynamic urban drainage simulators

Juan Pablo Carbajal, João Paulo Leitão, Carlo Albert +1

Many model based scientific and engineering methodologies, such as system identification, sensitivity analysis, optimization and control, require a large number of model evaluation…