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Giacomo Capodaglio

Postdoctoral Research Associate
CCS-2/CNLS

Numerical Methods for Partial Differential Equations and Nonlocal Models

Giacomo Capodaglio

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:

  1. G.Capodaglio and M. Petersen, Local time stepping for the shallow water equations in MPAS-Ocean , arXiv:2106.07154v1 (2021).
  2. 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).
  3. G. Capodaglio, M. D'Elia, P. Bochev and M. Gunzburger, An energy-based coupling approach to nonlocal interface problems, Computers & Fluids. 207,104593 (2020).
  4. 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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