Alessandra Menafoglio


Ricercatore (Assistant Professor)

Phone:+39 02 2399 4642
Fax: +39 02 2399
Office: Ed. 14 - Nave - VI floor
Email:

  • Available MOX Reports
  • Theses
  • Thesis Proposals
  • MOX Projects

Bonaventura, L.; Gatti F.; Menafoglio A.; Rossi D.; Brambilla D.; Papini M.; Longoni L.
An efficient and robust soil erosion model at the basin scale


Scimone, R.; Menafoglio, A.; Sangalli, L.m.; Secchi, P.
A look at the spatio-temporal mortality patterns in Italy during the COVID-19 pandemic through the lens of mortality densities


Scimone, R.; Taormina, T.; Colosimo, B. M.; Grasso, M.; Menafoglio, A.; Secchi, P.
Statistical modeling and monitoring of geometrical deviations in complex shapes with application to Additive Manufacturing


Torti, A.; Galvani, M.; Menafoglio, A.; Secchi, P.; Vantini S.
A General Bi-clustering Algorithm for Hilbert Data: Analysis of the Lombardy Railway Service


Peli, R.; Menafoglio, A.; Cervino, M.; Dovera, L.; Secchi, P;
Physics-based Residual Kriging for dynamically evolving functional random fields


Hron, K.; Machalova, J.; Menafoglio, A.
Bivariate densities in Bayes spaces: orthogonal decomposition and spline representation


Centofanti, F.; Lepore, A.; Menafoglio, A.; Palumbo, B.; Vantini, S.
Functional Regression Control Chart


Galvani, M.; Torti, A.; Menafoglio, A.; Vantini S.
FunCC: a new bi-clustering algorithm for functional data with misalignment


Caramenti, L.; Menafoglio, A.; Sgobba, S.; Lanzano, G.
Multi-Source Geographically Weighted Regression for Regionalized Ground-Motion Models


Didkovsky, O.; Ivanov, V.; Papini, M.; Longoni, L.; Menafoglio, A.
A comparison between machine learning and functional geostatistics approaches for data-driven analyses of solid transport in a pre-Alpine stream


Gatti, F.; Menafoglio, A.; Togni, N.; Bonaventura, L.; Brambilla, D.; Papini, M; Longoni, L.
A novel dowscaling procedure for compositional data in the Aitchison geometry with application to soil texture data


Menafoglio, A.; Sgobba, S.; Lanzano, G.; Pacor, F.
Simulation of seismic ground motion fields via object-oriented spatial statistics: a case study in Northern Italy


Bernardi, M.s.; Africa, P.c.; De Falco, C.; Formaggia, L.; Menafoglio, A.; Vantini, S.
On the Use of Interfeometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards


Didkovskyi, O.; Azzone, G.; Menafoglio A.; Secchi P.
Social and material vulnerability in the face of seismic hazard: an analysis of the Italian case


Menafoglio, A.; Secchi, P.
O2S2: a new venue for computational geostatistics


Capezza, C.; Lepore, A.; Menafoglio, A.; Palumbo, B.; Vantini, S.
Control charts for monitoring ship operating conditions and CO2 emissions based on scalar-on-function regression


Menafoglio, A.; Pigoli, D.; Secchi, P.
Kriging Riemannian Data via Random Domain Decompositions


Menafoglio, A.; Gaetani, G.; Secchi, P.
Random Domain Decompositions for object-oriented Kriging over complex domains


Menafoglio, A.; Grasso, M.; Secchi, P.; Colosimo, B.m.
Profile Monitoring of Probability Density Functions via Simplicial Functional PCA with application to Image Data


Grujic, O.; Menafoglio, A.; Guang, Y.; Caers, J.
Cokriging for multivariate Hilbert space valued random fields. Application to multifidelity computer code emulation


Talska, R.; Menafoglio, A.; Machalova, J.; Hron, K.; Fiserova, E.
Compositional regression with functional response


Menafoglio, A.; Hron, K.; Filzmoser, P.
Logratio approach to distributional modeling


Menafoglio, A.; Secchi, P.
Statistical analysis of complex and spatially dependent data: a review of Object Oriented Spatial Statistics


Menafoglio, A.; Guadagnini, A.; Secchi, P.
Stochastic Simulation of Soil Particle-Size Curves in Heterogeneous Aquifer Systems through a Bayes space approach


Menafoglio, A; Grujic, O.; Caers, J.
Universal kriging of functional data: trace-variography vs cross-variography? Application to forecasting in unconventional shales


Grasso, M.; Menafoglio, A.; Colosimo, B.m.; Secchi, P.
Using Curve Registration Information for Profile Monitoring


Menafoglio, A.; Petris, G.
Kriging for Hilbert-space valued random fields: the Operatorial point of view


Menafoglio, A.; Secchi, P.; Guadagnini, A.
A Class-Kriging predictor for Functional Compositions with Application to Particle-Size Curves in Heterogeneous Aquifers


Hron, K.; Menafoglio, A.; Templ, M.; Hruzova K.; Filzmoser, P.
Simplicial principal component analysis for density functions in Bayes spaces


Pigoli, D.; Menafoglio, A.; Secchi, P.
Kriging prediction for manifold-valued random field


Menafoglio, A; Guadagnini, A; Secchi, P
A Kriging Approach based on Aitchison Geometry for the Characterization of Particle-Size Curves in Heterogeneous Aquifers


Menafoglio, A.; Dalla Rosa, M.; Secchi, P.
A Universal Kriging predictor for spatially dependent functional data of a Hilbert Space


Application of multifidelity functional surrogate approaches to uncertainty quantification in subsurface modelling

Obiettivo:

L’obiettivo della tesi consiste nell’applicare la metodologia multifidelity functional surrogate modeling in ambito subsurface/reservoir per ottenere la quantificazione dell’incertezza delle previsioni basate su modello.  Si vuole inoltre confrontare il metodo con le tecniche più tradizionali di surrogate modeling utilizzate attualmente su un caso di complessità realistica.

 

Proposta possibili attività:

Le attività potrebbero organizzarsi secondo lo schema seguente:

  1. Revisione dello stato dell’arte basata sui lavori più recenti sviluppati sul tema in particolare facendo riferimento alle pubblicazioni [2] e [3] nella sezione references; 
  2. Familiarizzazione con 
    1. il prototipo di multifidelity surrogate modelling sviluppato in [2] che comprende sia l’approccio di Cokriging funzionale, sia alcune semplici tecniche di upscaling più comunemente utilizzate in ambito subsurface/reservoir ;
    2. la simulazione multifase di fluidi in mezzo poroso utilizzando inizialmente il tool open source OPM Flow;
  3. Gestione dell’incertezza su modello realistico attraverso opportuno campionamento e realizzazioni multiple utilizzando un simulatore industriale su sistemi HPC basati su GPU;
  4. Quantificazione dell’incertezza sia con approccio functional-multifidelity sia con metodi tradizionali e confronto;
  5. Stesura report con la discussione del confronto relativo al punto 4 e documentazione del codice prototipo; 

 

References

  1. Aliyev, E. and Durlofsky, L.J. “Multilevel field development optimization under uncertainty using a sequence of upscaled models. Mathematical Geosciences, 49(3), 307–339. 2017.
  2. M. Bezzegato: “Multifidelity surrogate modeling in reservoir simulation: a functional cokriging approach to uncertainty quantification and prediction”, M. Sc. in Mathematical Engineering Thesis, 2019;
  3. Grujic, O., Menafoglio, A., Yang, G. and Caers, J. “Cokriging for multivariate Hilbert space valued random fields: application to multi-fidelity computer code emulation”. Stochastic Environmental Research and Risk Assessment, 32(7), 1955–1971. 2018.
  4. Kostakis, F. F., Mallison, B. T., and Durlofsky, L. J.. "Multifidelity Framework For Uncertainty Quantification With Multiple Quantities Of Interest." ECMOR XVI-16th European Conference on the Mathematics of Oil Recovery. 2018.
  5. Thenon, A., Gervais, V.,  and Le Ravalec, M.. "Multi-fidelity meta-modeling for reservoir engineering-application to history matching." Computational Geosciences 20.6 (2016): 1231-1250.
  6. Thenon, A., Gervais V., Le Ravalec, M. "Multi-fidelity Proxy Models for Reservoir Engineering." ECMOR XV-15th European Conference on the Mathematics of Oil Recovery. 2016.