Gianluca Geraci

Biography

Gianluca is a Principal Member of the Technical Staff in the Optimization and Uncertainty Quantification department at Sandia. His current research interests span uncertainty quantification, multi-fidelity modeling, dimension reduction, and data-driven approaches. Gianluca’s work at Sandia focuses on the development of computational and data-driven algorithms for reliable predictions and analyses for scientific and engineering applications. Gianluca’s work at Sandia focuses on the development of computational and data-driven algorithms for reliable predictions and analyses for scientific and engineering applications. Since joining Sandia in 2016, Gianluca has worked on a variety of LDRD, NNSA, DARPA, and DOE funded projects involving a wide range of scientific problems and applications (e.g., internal and external aerodynamics, radiation transport, and computer networks). Gianluca received the DOE Early Career Research Award in 2024 to advance his research on enabling scientific data-driven modeling from heterogeneous, distributed, and multi-model datasets.

Gianluca regularly serves as reviewer for peer-reviewed journals (e.g., JCP, CMAME, IJUQ, SIAM/ASA JUQ) and has also served as Guest Editor for the International Journal for Uncertainty Quantification (2019-2021). He is currently serving as Chair of the Uncertainty Quantification (formerly Non-Deterministic Approaches) Technical Committee of the American Institute of Aeronautics and Astronautics (AIAA).

Initially supported by an INRIA@Silicon Valley fellowship, Gianluca worked at Stanford University under the direction of Prof. Gianluca Iaccarino on the PSAAP II project (2014-2016), where he focused on the characterization and propagation of uncertainties (e.g., particles’ polydispersity) and the assessment of the confidence in numerical simulations (including Direct Numerical Simulations) of solar receivers in which turbulence, radiation, and particle transport interact.

Gianluca carried out his Ph.D. thesis’ research work at the French Institute for Research in Computer Science and Automation (INRIA) focusing on the development of semi-intrusive uncertainty propagation schemes based on multi-resolution finite volume approaches in the combined physical/time/stochastic space for hyperbolic conservation laws. He also developed non-intrusive high-order global sensitivity analysis approaches and non-intrusive robust optimization strategies.

For his master thesis at the Polytechnic University of Milan, Gianluca developed hybrid finite element / finite volume schemes for the solution of Euler equations on unstructured meshes in orthogonal curvilinear coordinates (cylindrical/spherical).

Education

• Ph.D. in Applied Mathematics and Scientific Computing, University of Bordeaux, France, 2013. Mention: tres honorable.
  • Thesis title: “Schemes and Strategies to Propagate and Analyze Uncertainties in Computational Fluid Dynamics Applications”.
    • Advisor: Prof. Remi Abgrall, Co-Advisor: Dr. Pietro Marco Congedo
• Master Degree, Aeronautical Engineering (Major Aerodynamics), Polytechnic University of Milan, Italy, 2010, cum laude.
  • Thesis title: “Hybrid finite element/volume scheme for unstructured meshes in orthogonal curvilinear coordinates” (in Italian)
    • Advisor: Prof. Alberto Guardone
• Bachelor Degree, Aerospace Engineering, Polytechnic University of Milan, Italy, 2007.
  • Thesis title: “Characterization of shock absorbers with aluminum foam inserts” (in Italian)

Selected Journal Publications

Multifidelity UQ

• X. Zeng, G. Geraci, A.A. Gorodetsky, J.D. Jakeman, R. Ghanem, “Boosting efficiency and reducing graph reliance: Basis adaptation integration in Bayesian multi-fidelity networks.” Computer Methods in Applied Mechanics and Engineering, Vol. 436, 117657, 2025.
• A. Zanoni, G. Geraci, M. Salvador, K. Menon, A.L. Marsden, D.E. Schiavazzi, “Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques.” Computer Methods in Applied Mechanics and Engineering, Vol. 429, 117119, 2024.
• X. Zeng, G. Geraci, M.S. Eldred, J.D. Jakeman, A.A. Gorodetsky, R. Ghanem, “Multifidelity uncertainty quantification with models based on dissimilar parameters.” Computer Methods in Applied Mechanics and Engineering, Vol. 415, 116205, 2023.
• A. Gorodetsky, G. Geraci, M. S. Eldred, J. D. Jakeman, “A generalized approximate control variate framework for multifidelity uncertainty quantification.” Journal of Computational Physics, Vol. 408, 109257, 2020.

Sensitivity Analysis

• G. Geraci, P.M. Congedo, R. Abgrall and G. Iaccarino, “High-order statistics in global sensitivity analysis: decomposition and model reduction”. Computer Methods in Applied Mechanics and Engineering, Vol. 301, pp. 80-115, 2016.

UQ for Stochastic Solvers

• B.W. Reuter, G. Geraci, T.M. Wildey, “Analysis of the Challenges in Developing Sample-Based Multi-fidelity Estimators for Non-deterministic Models,” International Journal for Uncertainty Quantification, pp. 1-30, 2024.
• K.B. Clements, G. Geraci, A.J. Olson, T.S. Palmer, “A variance deconvolution estimator for efficient uncertainty quantification in Monte Carlo radiation transport applications”, Journal of Quantitative Spectroscopy and Radiative Transfer, Vol. 319, 108958, 2024.

Scientific Machine Learning

• O. Davis, G. Geraci, M. Motamed, “Deep Learning without Global Optimization by Random Fourier Neural Networks.” SIAM Journal on Scientific Computing, Vol. 47(2), 2025.
• A. Zanoni, G. Geraci, M. Salvador, A.L. Marsden, D.E. Schiavazzi, “Neural Active Manifolds: Nonlinear Dimensionality Reduction for Uncertainty Quantification.” Journal of Scientific Computing, Vol. 105, 79, 2025.
• L.H. Partin,G. Geraci, A. Rushdi, M.S. Eldred, D.E. Schiavazzi, “Multifidelity data fusion in convolutional encoder/decoder networks”. Journal of Computational Physics, Vol. 472, pp. 111666, 2023.
• A. A. Gorodetsky, J. D. Jakeman, G. Geraci, “MFNets: Learning data-driven networks for uncertainty quantification”. Computational Mechanics, Vol. 68, pp. 741–758, 2021.

Optimization

• F. Menhorn, G. Geraci, D.T. Seidl, Y.M. Marzouk, M.S. Eldred, H.-J. Bungartz, “Multilevel Monte Carlo Estimators for Derivative-Free Optimization Under Uncertainty”, Vol. 14(3), pp. 21-65, 2024.
• P. M. Congedo, G. Geraci, R. Abgrall, V. Pediroda, and L. Parussini, TSI metamodels-based multi-objective robust optimization. Engineering Computations, Vol. 30 (8), pp.1032 – 1053, 2013.

Semi-Intrusive UQ

• G. Geraci, R. Abgrall, P.M. Congedo and G. Iaccarino, “A Novel Weakly-Intrusive Non-linear Multiresolution Framework for Uncertainty Quantification in Hyperbolic Partial Differential Equations”. Journal of Scientific Computing, Vol. 66 (1), pp 358–405, 2016.
• R. Abgrall, P.M. Congedo, G. Geraci and M.G. Rodio, “Stochastic Discrete Equation Method (sDEM) for two-phase flows”. Journal of Computational Physics, Vol. 299, pp. 281-306, 2015.
• R. Abgrall, P.M. Congedo, G. Geraci and G. Iaccarino, “An adaptive multiresolution semi-intrusive scheme for UQ in compressible fluid problems”. International Journal of Numerical Methods in Fluids, John Wiley & Sons Ltd., Vol. 78 (10), pp. 595-637, 2015.

Hybrid Finite Element/Finite Volume

• D. De Santis, G. Geraci, and A. Guardone, “Equivalence conditions between linear Lagrangian finite element and node-centred finite volume schemes for conservation laws in cylindrical coordinates”. International Journal of Numerical Methods in Fluids, John Wiley & Sons Ltd., 74 (7), pp. 514-542, 2014.
• A. Guardone, D. De Santis, G. Geraci and M. Pasta, “On the relation between finite element and finite volume schemes for compressible flows with cylindrical and spherical symmetry”. Journal of Computational Physics, Vol. 230 (3), pp. 680-694, 2011.

Misc/Applications

• A. Shelley, A.J. Olson, G. Geraci, D.M. Tartakovsky, “Multipoint Correlations in Poisson Media”. Physical Review Letters, Vol. 135, pp. 156101, 2025.
• G. Geraci, J. Crussell, L.P. Swiler, B.J. Debusschere, “Exploration of Multifidelity UQ Sampling Strategies for Computer Network Applications”. International Journal for Uncertainty Quantification, Vol. 11(1), pp. 93-118, 2021.
• C. M. Fleeter, G. Geraci, D. E. Schiavazzi, A. M. Kahn, A. L. Mardsen, “Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics”. Computer Methods in Applied Mechanics and Engineering, Vol. 365, 113030, 2020.
• H. R. Fairbanks, L. Jofre, G. Geraci, G. Iaccarino, A. Doostan, “Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence”. Journal of Computational Physics, Vol. 402, 108996, 2020.
• X. Huan, C. Safta, K. Sargsyan, G. Geraci, M.S. Eldred, Z.P. Vane, G. Lacaze, J.C. Oefelein, H.N. Najm, “Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations”. AIAA Journal, Vol. 56, pp. 1170-1184, 2018.