25 citations · 32 across the 10 of their papers we have counts for
12 papers
Symplectic filtering of Hamiltonian dynamics with moving sensors
Olga Mula, Cecilia Pagliantini, Federico Vismara
While structure-preserving methods for the forward simulation of Hamiltonian systems are well established, the mathematical foundations of their data assimilation and filtering cou…
Symplectic Isospectral Runge--Kutta Methods as Lie group methods
Paolo Cifani, Klas Modin, Cecilia Pagliantini +1
We compare three approaches for structure preserving numerical integration of isospectral flows on quadratic Lie algebras. Such flows originate from Hamiltonian dynamics on the cot…
Adaptive hyper-reduction of non-sparse operators: application to parametric particle-based kinetic plasma models
Cecilia Pagliantini, Federico Vismara
This paper proposes an adaptive hyper-reduction method to reduce the computational cost associated with the simulation of parametric particle-based kinetic plasma models, specifica…
Geometric low-rank approximation of the Zeitlin model of incompressible fluids on the sphere
Cecilia Pagliantini
We consider the vorticity formulation of the Euler equations describing the flow of a two-dimensional incompressible ideal fluid on the sphere. Zeitlin's model provides a finite-di…
Conformal variational discretisation of infinite dimensional Hamiltonian systems with gradient flow dissipation
Damiano Lombardi, Cecilia Pagliantini
Nonconservative evolution problems describe irreversible processes and dissipative effects in a broad variety of phenomena. Such problems are often characterised by a conservative…
Dynamical approximation and sensor placement for filtering problems
Olga Mula, Cecilia Pagliantini, Federico Vismara
We consider the inverse problem of reconstructing an unknown function from a finite set of measurements, under the assumption that is the trajectory of a transport-dominate…
Fully adaptive structure-preserving hyper-reduction of parametric Hamiltonian systems
Cecilia Pagliantini, Federico Vismara
Model order reduction provides low-complexity high-fidelity surrogate models that allow rapid and accurate solutions of parametric differential equations. The development of reduce…
Gradient-preserving hyper-reduction of nonlinear dynamical systems via discrete empirical interpolation
Cecilia Pagliantini, Federico Vismara
This work proposes a hyper-reduction method for nonlinear parametric dynamical systems characterized by gradient fields such as Hamiltonian systems and gradient flows. The gradient…
Adaptive symplectic model order reduction of parametric particle-based Vlasov-Poisson equation
Jan S. Hesthaven, Cecilia Pagliantini, Nicolò Ripamonti
High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerba…
Structure-preserving model order reduction of Hamiltonian systems
J. S. Hesthaven, C. Pagliantini, N. Ripamonti
We discuss the recent developments of projection-based model order reduction (MOR) techniques targeting Hamiltonian problems. Hamilton's principle completely characterizes many hig…
Dynamical reduced basis methods for Hamiltonian systems
Cecilia Pagliantini
We consider model order reduction of parameterized Hamiltonian systems describing nondissipative phenomena, like wave-type and transport dominated problems. The development of redu…
Rank-adaptive structure-preserving model order reduction of Hamiltonian systems
Jan S. Hesthaven, Cecilia Pagliantini, Nicolò Ripamonti
This work proposes an adaptive structure-preserving model order reduction method for finite-dimensional parametrized Hamiltonian systems modeling non-dissipative phenomena. To over…