My research deals with ergodic theory for stochastic PDEs. I am particularly driven by problems with applications to geophysical fluid dynamics, weather and climate. I am also interested in the application of statistical mechanics techniques to these problems.
Since August 2024 I work as a postdoc with professor Glatt-Holtz (IUB) focusing on the application of techniques from infinite dimensional ergodic theory to Monte Carlo methods and Bayesian inverse problems.
F. Fornasaro, T. Kuna, G. Carigi, Well-posedness and long time dynamics for a quasi-geostrophic ocean-atmosphere model with radiation balance, (2025) ArXiv
M. Santos Gutiérrez, N. Zagli, G. Carigi, Markov matrix perturbations to optimize dynamical and entropy functionals, (2025) ArXiv
J. Broecker, G. Carigi, T. Kuna, V.R.Martinez, Reconstruction of wide spectrum forcing in transport-diffusion and Navier-Stokes equations, (2025) ArXiv
G. Carigi, T. Kuna, J. Broecker, Linear and fractional response for nonlinear dissipative SPDEs, Nonlinearity, 37 105002 (2024) https://doi.org/10.1088/1361-6544/ad6bdd
G. Carigi, E. Luongo, Dissipation properties of transport noise in the two-layer quasi-geostrophic model, J. Math. Fluid Mech., 25, 28 (2023) https://doi.org/10.1007/s00021-023-00773-z
G. Carigi, J. Broecker, T. Kuna, Exponential ergodicity for a stochastic two–layer quasi–geostrophic model, Stochastics and Dynamics, (2022), https://doi.org/10.1142/S0219493723500119
Hanlon, H.M., Bernie, D., Carigi, G. et al. Future changes to high impact weather in the UK, Climatic Change, 166, 50 (2021) https://doi.org/10.1007/s10584-021-03100-5 [ Read the Met Office Press Release on this study here ]
Carigi, G. (2021) Ergodic properties and response theory for a stochastic two–layer model of geophysical fluid dynamics. PhD thesis, University of Reading, doi.org/10.48683/1926.00102181
J. Bröcker, P. Cannarsa, G. Carigi, T. Kuna, C. Urbani, A stochastic two-layer Energy Balance Model: well-posedness and exponential ergodicity
G. Carigi, N.E. Glatt-Holtz, C.F. Mondaini, On the mixing properties of some preconditioned multiproposal Markov Chain Monte Carlo algorithms
Department of Statistics
Swain Hall East Room 311
Indiana University
Bloomington, IN, USA