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Sysoeva E. Wilson loops and its correlators with chiral operators in $\mathcalN=2, 4$ SCFT at large $N$. JHEP. 2018 ;03:155.
Sysoeva E. Wilson loop and its correlators in the limit of large coupling constant. Nucl. Phys. B. 2018 ;936:383–399.
Stupovski B, Torres R. Existence of Riemannian metrics with positive biorthogonal curvature on simply connected 5-manifolds. Archiv der Mathematik [Internet]. 2020 :1–9. Available from: https://dx.doi.org/10.1007/s00013-020-01511-x
Strazzullo M, Ballarin F, Rozza G. A CERTIFIED REDUCED BASIS Method FOR LINEAR PARAMETRIZED PARABOLIC OPTIMAL CONTROL PROBLEMS IN SPACE-TIME FORMULATION. 2021 .
Strazzullo M, Zainib Z, Ballarin F, Rozza G. Reduced Order Methods for Parametrized Non-linear and Time Dependent Optimal Flow Control Problems, Towards Applications in Biomedical and Environmental Sciences. In: Numerical Mathematics and Advanced Applications ENUMATH 2019. Vol. 139. Numerical Mathematics and Advanced Applications ENUMATH 2019. Springer; 2021. pp. 841–850. Available from: https://arxiv.org/abs/1912.07886
Strazzullo M, Ballarin F, Rozza G. POD-Galerkin Model Order Reduction for Parametrized Nonlinear Time Dependent Optimal Flow Control: an Application to Shallow Water Equations. 2020 .
Strazzullo M, Ballarin F, Rozza G. POD–Galerkin Model Order Reduction for Parametrized Time Dependent Linear Quadratic Optimal Control Problems in Saddle Point Formulation. Journal of Scientific Computing. 2020 ;83.
Strazzullo M. Model Order Reduction for Nonlinear and Time-Dependent Parametric Optimal Flow Control Problems. 2021 .
Strazzullo M, Zainib Z, Ballarin F, Rozza G. Reduced Order Methods for Parametrized Non-linear and Time Dependent Optimal Flow Control Problems, Towards Applications in Biomedical and Environmental Sciences. In: Vermolen FJ, Vuik C Numerical Mathematics and Advanced Applications ENUMATH 2019. Numerical Mathematics and Advanced Applications ENUMATH 2019. Cham: Springer International Publishing; 2021. Available from: https://www.springerprofessional.de/en/reduced-order-methods-for-parametrized-non-linear-and-time-depen/19122676
Strazzullo M, Girfoglio M, Ballarin F, Iliescu T, Rozza G. Consistency of the full and reduced order models for Evolve-Filter-Relax Regularization of Convection-Dominated, Marginally-Resolved Flows. 2021 .
Strazzullo M, Ballarin F, Mosetti R, Rozza G. Model Reduction for Parametrized Optimal Control Problems in Environmental Marine Sciences and Engineering. SIAM Journal on Scientific Computing [Internet]. 2018 ;40:B1055-B1079. Available from: https://doi.org/10.1137/17M1150591
Star K, Stabile G, Georgaka S, Belloni F, Rozza G, Degroote J. POD-Galerkin Reduced Order Model of the Boussinesq Approximation for Buoyancy-Driven Enclosed Flows. In: International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019. International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019. ; 2019.
Star K, Stabile G, Rozza G, Degroote J. A POD-Galerkin reduced order model of a turbulent convective buoyant flow of sodium over a backward-facing step. Applied Mathematical Modelling. 2021 ;89:486-503.
Star K, Sanderse B, Stabile G, Rozza G, Degroote J. Reduced order models for the incompressible Navier-Stokes equations on collocated grids using a `discretize-then-project' approach. International Journal for Numerical Methods in Fluids [Internet]. 2021 ;93:2694–2722. Available from: https://doi.org/10.1002/fld.4994
Star K, Stabile G, Belloni F, Rozza G, Degroote J. A novel iterative penalty method to enforce boundary conditions in Finite Volume POD-Galerkin reduced order models for fluid dynamics problems. Communications in Computational Physics. 2021 ;30:34–66.
Stabile G, Zancanaro M, Rozza G. Efficient Geometrical parametrization for finite-volume based reduced order methods. International Journal for Numerical Methods in Engineering [Internet]. 2020 ;121:2655-2682. Available from: https://arxiv.org/abs/1901.06373
Stabile G, Ballarin F, Zuccarino G, Rozza G. A reduced order variational multiscale approach for turbulent flows. Advances in Computational Mathematics [Internet]. 2019 ;45:2349-2368. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068076665&doi=10.1007%2fs10444-019-09712-x&partnerID=40&md5=af0142e6d13bbc2e88c6f31750aef6ad
Stabile G, Matthies HG, Borri C. A novel reduced order model for vortex induced vibrations of long flexible cylinders. [Internet]. 2018 ;156:191–207. Available from: https://doi.org/10.1016/j.oceaneng.2018.02.064
Stabile G, Matthies HG, Borri C. A Reduced Order Model for the Simulation of Mooring Cable Dynamics. In: Computational Methods in Marine Engineering VI – MARINE2015. Computational Methods in Marine Engineering VI – MARINE2015. Salvatore, Francesco; Broglia, Riccardo; Muscari, Roberto; 2015. pp. 387–400.
Stabile G, Hijazi S, Mola A, Lorenzi S, Rozza G. POD-Galerkin reduced order methods for CFD using Finite Volume Discretisation: vortex shedding around a circular cylinder. Communications in Applied and Industrial Mathematics. 2017 ;8:210-236.
Stabile G, Rozza G. Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equations. Computers and Fluids [Internet]. 2018 ;173:273-284. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043366603&doi=10.1016%2fj.compfluid.2018.01.035&partnerID=40&md5=c15435ea3b632e55450da19ba2bb6125
Stabile G, Rosic B. Bayesian identification of a projection-based reduced order model for computational fluid dynamics. Computers & Fluids. 2020 ;201:104477.
Soranzo N, Zampieri M, Farina L, Altafini C. mRNA stability and the unfolding of gene expression in the long-period yeast metabolic cycle. BMC Systems Biology (2009) 3:18 [Internet]. 2009 . Available from: http://hdl.handle.net/1963/3630
Soranzo N, Altafini C. ERNEST: a toolbox for chemical reaction network theory. Bioinformatics 25 (2009) 2853-2854 [Internet]. 2009 . Available from: http://hdl.handle.net/1963/3826
Soranzo N, Ramezani F, Iacono G, Altafini C. Decompositions of large-scale biological systems based on dynamical properties. Bioinformatics (Oxford, England). 2012 Jan; 28(1):76-83 [Internet]. 2012 . Available from: http://hdl.handle.net/1963/5226

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