Giacomo CapodaglioPostdoctoral Research Associate
CCS-2/CNLS Numerical Methods for Partial Differential Equations and Nonlocal Models 
Office: Telework-CCS
Mail Stop: D413
home page Research highlight- Local time stepping schemes for the Model for Prediction Across Scales (MPAS)
- Domain decomposition and interface models for nonlocal diffusion problems
- Density estimation methods for partial differential equations with random parameters
- Coupled finite element method and material point method for solid-solid and fluid-solid interaction simulation
- Multigrid methods for finite element applications with hanging nodes
|  | Educational Background/Employment:- PhD in Applied Mathematics (2018), Texas Tech University
- Ms in Energy Engineering (2013), University of Bologna
- Bs in Energy Engineering (2010), University of Bologna
- Employment:
- 2020 - Present, Postdoctoral Research Associate, Los Alamos National Laboratory, Los Alamos, NM, U.S.A.
- 2018 - 2020, Postdoctoral Research Associate, Florida State University, Tallahassee, FL, U.S.A.
Research Interests: - Computational Mathematics
- Discretization Methods and Solvers for PDEs
- Nonlocal Models
- Uncertainty Quantification
- Particle Tracking Methods
Selected Recent Publications: - G.Capodaglio and M. Petersen, Local time stepping for the shallow water equations in MPAS-Ocean , arXiv:2106.07154v1 (2021).
- G. Capodaglio, M. D'Elia, M. Gunzburger, P. Bochev, M. Klar, and C. Vollmann, A general framework for substructuring-based domain decomposition methods for models having nonlocal interactions, Numerical Methods for Partial Differential Equations. doi.org/10.1002/num.22832 (2021).
- G. Capodaglio, M. D'Elia, P. Bochev and M. Gunzburger, An energy-based coupling approach to nonlocal interface problems, Computers & Fluids. 207,104593 (2020).
- G. Capodaglio and M. Gunzburger, Piecewise polynomial approximation of probability density functions with application to uncertainty quantification for stochastic PDEs , Quantification of Uncertainty: Improving Efficiency and Technology. Lecture Notes in Computational Science and Engineering . 137, pp 101-127 (2020).
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