Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Cenedese, Mattia"'
Autor:
Xu, Zhenwei, Kaszás, Bálint, Cenedese, Mattia, Berti, Giovanni, Coletti, Filippo, Haller, George
We use video footage of a water tunnel experiment to construct a two-dimensional reduced-order model of the flapping dynamics of an inverted flag in uniform flow. The model is obtained as the reduced dynamics on an attracting spectral submanifold (SS
Externí odkaz:
http://arxiv.org/abs/2402.08504
We develop a model reduction technique for non-smooth dynamical systems using spectral submanifolds. Specifically, we construct low-dimensional, sparse, nonlinear and non-smooth models on unions of slow and attracting spectral submanifolds (SSMs) for
Externí odkaz:
http://arxiv.org/abs/2312.14927
Autor:
Alora, John Irvin, Pabon, Luis A., Köhler, Johannes, Cenedese, Mattia, Schmerling, Ed, Zeilinger, Melanie N., Haller, George, Pavone, Marco
Real-world systems are often characterized by high-dimensional nonlinear dynamics, making them challenging to control in real time. While reduced-order models (ROMs) are frequently employed in model-based control schemes, dimensionality reduction int
Externí odkaz:
http://arxiv.org/abs/2309.05746
Modeling and control of high-dimensional, nonlinear robotic systems remains a challenging task. While various model- and learning-based approaches have been proposed to address these challenges, they broadly lack generalizability to different control
Externí odkaz:
http://arxiv.org/abs/2209.05712
We present a fast method for nonlinear data-driven model reduction of dynamical systems onto their slowest nonresonant spectral submanifolds (SSMs). We use observed data to locate a low-dimensional, attracting slow SSM and compute a maximally sparse
Externí odkaz:
http://arxiv.org/abs/2204.14169
Publikováno v:
B. Kasz\'as, M. Cenedese, G. Haller, Dynamics-based machine learning of transitions in Couette flow, Phys. Rev. Fluids 7, L082402 (2022)
We derive low-dimensional, data-driven models for transitions among exact coherent states (ECSs) in one of the most studied canonical shear flows, the plane Couette flow. These one- or two-dimensional nonlinear models represent the leading-order redu
Externí odkaz:
http://arxiv.org/abs/2203.13098
We develop a methodology to construct low-dimensional predictive models from data sets representing essentially nonlinear (or non-linearizable) dynamical systems with a hyperbolic linear part that are subject to external forcing with finitely many fr
Externí odkaz:
http://arxiv.org/abs/2201.04976
While data-driven model reduction techniques are well-established for linearizable mechanical systems, general approaches to reducing non-linearizable systems with multiple coexisting steady states have been unavailable. In this paper, we review such
Externí odkaz:
http://arxiv.org/abs/2110.01929
Publikováno v:
In International Journal of Non-Linear Mechanics July 2024 163
Autor:
Cenedese, Mattia, Haller, George
Publikováno v:
Chaos 30, 083103 (2020)
Frequency responses of multi-degree-of-freedom mechanical systems with weak forcing and damping can be studied as perturbations from their conservative limit. Specifically, recent results show how bifurcations near resonances can be predicted analyti
Externí odkaz:
http://arxiv.org/abs/2005.00444