71 citations · 420 across the 40 of their papers we have counts for
8 papers · 1 filter
Combining noisy well data and expert knowledge in a Bayesian calibration of a flow model under uncertainties: an application to solute transport in the Ticino basin
Emily A. Baker, Sauro Manenti, Alessandro Reali +3
Groundwater flow modeling is commonly used to calculate groundwater heads, estimate groundwater flow paths and travel times, and provide insights into solute transport processes wi…
Sparse-grids uncertainty quantification of part-scale additive manufacturing processes
Mihaela Chiappetta, Chiara Piazzola, Massimo Carraturo +3
The present paper aims at applying uncertainty quantification methodologies to process simulations of powder bed fusion of metal. In particular, for a part-scale thermomechanical m…
Combining the Morris Method and Multiple Error Metrics to Assess Aquifer Characteristics and Recharge in the Lower Ticino Basin, in Italy
Emily A. Baker, Alessandro Cappato, Sara Todeschini +4
Groundwater flow model accuracy is often limited by the uncertainty in model parameters that characterize aquifer properties and aquifer recharge. Aquifer properties such as hydrau…
Modeling nonlocal behavior in epidemics via a reaction-diffusion system incorporating population movement along a network
Malú Grave, Alex Viguerie, Gabriel F. Barros +3
The outbreak of COVID-19, beginning in 2019 and continuing through the time of writing, has led to renewed interest in the mathematical modeling of infectious disease. Recent works…
Well-posedness for a diffusion-reaction compartmental model simulating the spread of COVID-19
Ferdinando Auricchio, Pierluigi Colli, Gianni Gilardi +2
This paper is concerned with the well-posedness of a diffusion-reaction system for a Susceptible-Exposed-Infected-Recovered (SEIR) mathematical model. This model is written in term…
Data-driven simulation of Fisher-Kolmogorov tumor growth models using Dynamic Mode Decomposition
Alex Viguerie, Malú Grave, Gabriel F. Barros +3
The computer simulation of organ-scale biomechanistic models of cancer personalized via routinely collected clinical and imaging data enables to obtain patient-specific predictions…