Zobrazeno 1 - 10
of 219
pro vyhledávání: '"Frangi, Attilio"'
Autor:
Conti, Paolo, Kneifl, Jonas, Manzoni, Andrea, Frangi, Attilio, Fehr, Jörg, Brunton, Steven L., Kutz, J. Nathan
The simulation of many complex phenomena in engineering and science requires solving expensive, high-dimensional systems of partial differential equations (PDEs). To circumvent this, reduced-order models (ROMs) have been developed to speed up computa
Externí odkaz:
http://arxiv.org/abs/2405.20905
Measured data from a dynamical system can be assimilated into a predictive model by means of Kalman filters. Nonlinear extensions of the Kalman filter, such as the Extended Kalman Filter (EKF), are required to enable the joint estimation of (possibly
Externí odkaz:
http://arxiv.org/abs/2404.07536
This paper presents the first application of the direct parametrisation method for invariant manifolds to a fully coupled multiphysics problem involving the nonlinear vibrations of deformable structures subjected to an electrostatic field. The formul
Externí odkaz:
http://arxiv.org/abs/2312.14803
Autor:
Conti, Paolo, Guo, Mengwu, Manzoni, Andrea, Frangi, Attilio, Brunton, Steven L., Kutz, J. Nathan
High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations considered and/or time window evaluated for modeling a given system. M
Externí odkaz:
http://arxiv.org/abs/2309.00325
Publikováno v:
Vizzaccaro, Alessandra, et al. "Direct parametrisation of invariant manifolds for non-autonomous forced systems including superharmonic resonances." Nonlinear Dynamics (2024): 1-36
The direct parametrisation method for invariant manifold is a model-order reduction technique that can be applied to nonlinear systems described by PDEs and discretised e.g. with a finite element procedure in order to derive efficient reduced-order m
Externí odkaz:
http://arxiv.org/abs/2306.09860
This paper presents a novel derivation of the direct parametrisation method for invariant manifolds able to build simulation-free reduced-order models for nonlinear piezoelectric structures, with a particular emphasis on applications to Micro-Electro
Externí odkaz:
http://arxiv.org/abs/2306.07540
Highly accurate simulations of complex phenomena governed by partial differential equations (PDEs) typically require intrusive methods and entail expensive computational costs, which might become prohibitive when approximating steady-state solutions
Externí odkaz:
http://arxiv.org/abs/2211.06786
Publikováno v:
Sensors, 2023, 23, no. 6: 3001
Micro-Electro-Mechanical-Systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless applications. Starting from full-order representations, we apply deep lea
Externí odkaz:
http://arxiv.org/abs/2205.05928
We propose a non-intrusive Deep Learning-based Reduced Order Model (DL-ROM) capable of capturing the complex dynamics of mechanical systems showing inertia and geometric nonlinearities. In the first phase, a limited number of high fidelity snapshots
Externí odkaz:
http://arxiv.org/abs/2111.12511
This paper investigates model-order reduction methods for geometrically nonlinear structures. The parametrisation method of invariant manifolds is used and adapted to the case of mechanical systems expressed in the physical basis, so that the techniq
Externí odkaz:
http://arxiv.org/abs/2109.10031