6 papers
Cluster Attention Neural Operators for Solving Parametric Partial Differential Equations
Ming Zhong, Antonio Colanera, Gianluigi Rozza +1
Traditional simulations of parametric partial differential equations (PDEs) rely on repetitive computations for each parameter, which makes high-fidelity design impractical. Neural…
Fuzzy local reduced order models (fl-ROMs)
Antonio Colanera, Luca Magri
Nonlinear dynamical systems often evolve on low-dimensional manifolds with intricate geometry, making them difficult to approximate with a single global reduced-order model (ROM).…
Adjoint-based optimization with quantized local reduced-order models for spatiotemporally chaotic systems
Defne E. Ozan, Antonio Colanera, Luca Magri
We introduce a computationally efficient and accurate reduced order modelling approach for the optimization of spatiotemporally chaotic systems. The proposed method combines quanti…
Towards extreme event prediction of turbulent flows with quantized local reduced-order models
Antonio Colanera, Luca Magri
This work develops quantized local reduced-order models (ql-ROMs) of the turbulent Minimal Flow Unit (MFU) for the analysis and interpretation of intermittent dissipative dynamics…
Quantized local reduced-order modeling in time (ql-ROM)
Antonio Colanera, Luca Magri
Spatiotemporally chaotic systems, such as the solutions of some nonlinear partial differential equations, are dynamical systems that evolve toward a lower dimensional manifold. Thi…
Orbital cluster-based network modelling
Antonio Colanera, Nan Deng, Matteo Chiatto +2
We propose a novel reduced-order methodology to describe complex multi-frequency fluid dynamics from time-resolved snapshot data. Starting point is the Cluster-based Network Model…