Laura Scarabosio

Preprints

W. van Harten, L. Scarabosio, Distributed preconditioning for the parametric Helmholtz equation.

S. Kuijpers, L. Scarabosio, Wavenumber-Explicit Well-Posedness of Bayesian Shape Inversion in Acoustic Scattering.

J. Careaga, P. Korevaar, V. Nikolić, L. Scarabosio, Application of the thin-film equations in modelling of Marangoni flow patterns amongst surfactant source and drain locations.

S. Schönfeld, L. Scarabosio, A. Ozkan, M.N. Rylander, C. Kuttler, Using systemic modeling and Bayesian calibration to investigate the role of the tumor microenvironment on chemoresistance.

Articles in journals

W. van Harten, L. Scarabosio, Exploiting locality in sparse polynomial approximation of parametric elliptic PDEs and application to parameterized domains, ESAIM: Mathematical Modelling and Numerical Analysis, 2024, 58(5), 1581-1613.

L. Scarabosio, Deep neural network surrogates for non-smooth quantities of interest in shape uncertainty quantification, SIAM/ASA Journal on Uncertainty Quantification, 2022, 10(3), 975-1011. Preprint

S. Schönfeld, A. Ozkan, L. Scarabosio, M. N. Rylander, C. Kuttler Environmental stress level to model tumor cell growth and survival, Mathematical Biosciences and Engineering, 2022, 19(6): 5509-5545.

M. Fritz, C. Kuttler, M.L. Rajendran, L. Scarabosio, B. Wohlmuth, On a subdiffusive tumour growth model with fractional time derivative, IMA Journal of Applied Mathematics 86.4 (2021): 688-729. Preprint

L. Scarabosio, B. Wohlmuth, J.T. Oden, D. Faghihi, Goal-oriented adaptive modeling of random heterogeneous media and model-based multilevel Monte Carlo methods, Computers & Mathematics with Applications 78.8 (2019): 2700-2718. Preprint

U. Khristenko, L. Scarabosio, P. Swierczynski, E. Ullmann, B. Wohlmuth, Analysis of Boundary Effects on PDE-Based Sampling of Whittle--Matérn Random Fields, SIAM/ASA Journal on Uncertainty Quantification 7 (3), 948-974. Preprint

L. Scarabosio, Multilevel Monte Carlo on a high-dimensional parameter space for transmission problems with geometric uncertainties, International Journal for Uncertainty Quantification 9.6 (2019). Preprint

R. Hiptmair, L. Scarabosio, C. Schillings, C. Schwab, Large deformation shape uncertainty quantification in acoustic scattering, Advances in Computational Mathematics, 1-44. Preprint

E.A.B.F. Lima, J.T. Oden, B. Wohlmuth, A. Shahmoradi, D.A. Hormuth II, T.E. Yankeelov, L. Scarabosio, T. Horger, Selection and validation of predictive models of radiation effects on tumor growth based on noninvasive imaging data, Computer methods in applied mechanics and engineering 327, 277-305. Preprint

A. Paganini, L. Scarabosio, R. Hiptmair, I. Tsukerman, Trefftz approximations: a new framework for nonreflecting boundary conditions, IEEE Transactions on Magnetics 52 (3), 1-4.

Peer-reviewed proceedings

J. de Lange, S. Rieken, L. Scarabosio, G. Lord, Preventing congestion management by modelling cable temperatures: a real-world case, IET Conference Proceedings CP876, Vol. 2024, No 5.

Theses

L. Scarabosio, Shape uncertainty quantification for scattering transmission problems, Diss. ETH Zurich, 2016.

Organization

Strategic Research Initiative "Advancing Mathematical Methods for Wave Phenomena" (2024-2026), with J. Koellermeier, V. Nikolić, Carlos Pérez-Arancibia, Carolina Urzúa-Torres.

CWI Semester Programme "Uncertainty Quantification for High-Dimensional Problems" in Fall 2024, with W. Edeling, O. Mula, P. Coveney, R. Dwight.

Member of organizing committee for Woudschoten 2024 (25-27 September).

Radboud Summer School "Quantifying Uncertainty: Prediction and Inverse Problems" (8-12 August 2022).

Workshop "Nonlinear PDEs: Analysis & Simulation", with R. Cristoferi and V. Nikolić (24-25 March 2022)

Applied Analysis Seminar, Radboud University, with V. Nikolić.

Conference minisymposia:

• "Learning High-Dimensional Functions: Approximation, Sampling, and Algorithms" at SIAM UQ24, with S. Brugiapaglia, N. Dexter, W. van Harten.
• "Exploring Complexity in Life Sciences with Modeling and Simulations" at SIAM CSE23, with D. Avitabile, F. Cavallini, G. Lord.
• "Shape Uncertainty Quantification meets Shape Statistics" at SIAM UQ22, with M. Zhang.
• "Efficient simulation of random fields and applications" at ENUMATH2019, with K. Podgórski.

Recent invited talks

Starting from March 2024

95th GAMM annual meeting, Poznan, "Bayesian shape inversion in time-harmonic scattering" (March 2025).

Weekly seminar of EDDy RTG, RWTH Aachen University, "Quantifying geometric uncertainties in PDE-based models" (January 2025).

SciCOMP seminar, Durham University, "Forward and inverse shape uncertainty quantification with partial differential equations" (November 2024).

Workshop "The Numerical Brain", VU Amsterdam, "Forward and inverse shape uncertainty quantification with physics-based models" (October 2024).

MCQMC 2024, University of Waterloo, "Bayesian shape inversion in acoustic and electromagnetic scattering" (August 2024).

Workshop at Mittag-Leffler Institute, "Exploiting locality in surrogates for solutions to PDEs on parameterized domains" (June 2024).

Joint VU/Leiden/Delft seminar, "A seamless integration of models and data to predict and understand tumor cell dynamics" (April 2024).

94th GAMM annual meeting, Magdeburg, "Surrogate models for shape uncertainty quantification" (March 2024).

Postdocs

Jan 2023 - Aug 2023: Julio Careaga, with V. Nikolić.

PhD students

since Jan 2023: Jordi de Lange, sponsored by Alliander, as daily supervisor.

since Nov 2021: Wouter van Harten.

May 2019 - Dec 2023: Sabrina Schönfeld, "Environmental stress level - a mathematical modeling framework to investigate the influence of the microenvironment on tumor cell survival", with C. Kuttler. Link to PhD thesis.

Master theses

since Mar 2025: Jos Aalberts, internship with TenneT.

since Sep 2024: Simon Geritz, internship with NLR.

Feb 2024 - Feb 2025: Max Hofman, "Preconditioner Selection and Placement - Iteratively solving the heat equation in parametric studies for grid management", with W. van Harten, internship with Alliander (team of S. Rieken).

Feb 2024 - Nov 2024: Daan Jansen, "Neural Network Approximations of Parametric PDEs".

Jan 2023 - Jul 2023: Safiere Kuijpers, "Bayesian Shape Inversion for Scattering Transmission Problems", with W. van Harten. Link to thesis.

Jan 2023 - Jul 2023: Dirk Heldens, "Reducing uncertainty in cable temperature estimation", internship with Alliander (team of S. Rieken).

Jan 2020 - Jul 2020: Alexandra Starostina, "Robin boundary conditions for PDE-based sampling of Gaussian Random Fields".

Bachelor theses

since Feb 2025: Dieke van der Heijden.

since Jun 2024: Aniek Reinders.

Feb 2024 - Jul 2024: Janna van Assen, "Deep learning as optimal control problem".

Jan 2024 - Jun 2024: Houssam Larhdaf, "Stock price forcasting: a Kalman filter approach", with G. Lord.

Jan 2022 - Dec 2022: Laura van Leuven, "Gradient descent methods and their use in machine learning".

Spring 2025: Monte Carlo Methods,, Radboud University.
       Calculus B, with V. Nikolić, Radboud University.

Fall 2024: Numerieke Methoden, Radboud University.

Spring 2024: Monte Carlo Methods, Radboud University.

Fall 2023: Numerieke Methoden, Radboud University.
       Bachelor seminar, with S. Tijssen, Radboud University.

Spring 2023: Monte Carlo Methods, Radboud University.

Fall 2022: Bachelor seminar, with S. Tijssen, Radboud University.

Spring 2022: Monte Carlo Methods, Radboud University.

Fall 2021: Bachelor seminar, with P. Hochs, Radboud University.

Spring 2021: Monte Carlo Methods, Radboud University.

Fall 2020: Bachelor seminar, with P. Hochs, Radboud University.

Fall 2019: Einführung in die Programmierung, TU Munich.

Fall 2017: Einführung in die Programmierung, TU Munich.

2016 - 2020: teaching assistant for various courses in numerical analyis at TU Munich.

2012 - 2016: teaching assistant for various courses in numerical analysis at ETH Zürich.

Professional experience

since Aug 2020: assistant professor, IMAPP, Radboud University (Netherlands).

Sep 2016 - Jul 2020: postdoc, Chair of Numerical Analysis, TU Munich (Germany).

May 2016 - Aug 2016: scientific assistant, Seminar for Applied Mathematics, ETH Zürich (Switzerland).

Education

Feb 2012 - May 2016: PhD in Mathematics, Seminar for Applied Mathematics, ETH Zürich (Switzerland).

Oct 2009 - Dic 2011: Master in Mathematical Modelling in Engineering, Polytechnic of Turin (Italy).

Sep 2006 - Oct 2009: Bachelor in Mathematics in Engineering, Polytechnic of Turin (Italy).