Deep learning helicopter dynamics models
We consider the problem of system identification of helicopter dynamics. Helicopters are
complex systems, coupling rigid body dynamics with aerodynamics, engine dynamics …
complex systems, coupling rigid body dynamics with aerodynamics, engine dynamics …
Data-driven spectral decomposition and forecasting of ergodic dynamical systems
D Giannakis - Applied and Computational Harmonic Analysis, 2019 - Elsevier
We develop a framework for dimension reduction, mode decomposition, and nonparametric
forecasting of data generated by ergodic dynamical systems. This framework is based on a …
forecasting of data generated by ergodic dynamical systems. This framework is based on a …
[图书][B] Dimensions, embeddings, and attractors
JC Robinson - 2010 - books.google.com
This accessible research monograph investigates how'finite-dimensional'sets can be
embedded into finite-dimensional Euclidean spaces. The first part brings together a number …
embedded into finite-dimensional Euclidean spaces. The first part brings together a number …
Chatter detection in turning using persistent homology
FA Khasawneh, E Munch - Mechanical Systems and Signal Processing, 2016 - Elsevier
This paper describes a new approach for ascertaining the stability of stochastic dynamical
systems in their parameter space by examining their time series using topological data …
systems in their parameter space by examining their time series using topological data …
Operator-theoretic framework for forecasting nonlinear time series with kernel analog techniques
R Alexander, D Giannakis - Physica D: Nonlinear Phenomena, 2020 - Elsevier
Kernel analog forecasting (KAF), alternatively known as kernel principal component
regression, is a kernel method used for nonparametric statistical forecasting of dynamically …
regression, is a kernel method used for nonparametric statistical forecasting of dynamically …
Attractors and Finite‐Dimensional Behaviour in the 2D Navier‐Stokes Equations
JC Robinson - International Scholarly Research Notices, 2013 - Wiley Online Library
The purpose of this review is to give a broad outline of the dynamical systems approach to
the two‐dimensional Navier‐Stokes equations. This example has led to much of the theory …
the two‐dimensional Navier‐Stokes equations. This example has led to much of the theory …
Learning stable deep dynamics models for partially observed or delayed dynamical systems
A Schlaginhaufen, P Wenk… - Advances in Neural …, 2021 - proceedings.neurips.cc
Learning how complex dynamical systems evolve over time is a key challenge in system
identification. For safety critical systems, it is often crucial that the learned model is …
identification. For safety critical systems, it is often crucial that the learned model is …
Transition manifolds of complex metastable systems: Theory and data-driven computation of effective dynamics
We consider complex dynamical systems showing metastable behavior, but no local
separation of fast and slow time scales. The article raises the question of whether such …
separation of fast and slow time scales. The article raises the question of whether such …
Revealing trends and persistent cycles of non-autonomous systems with autonomous operator-theoretic techniques
An important problem in modern applied science is to characterize the behavior of systems
with complex internal dynamics subjected to external forcings. Many existing approaches …
with complex internal dynamics subjected to external forcings. Many existing approaches …
Multivariate EMD-based modeling and forecasting of crude oil price
Recent empirical studies reveal evidence of the co-existence of heterogeneous data
characteristics distinguishable by time scale in the movement crude oil prices. In this paper …
characteristics distinguishable by time scale in the movement crude oil prices. In this paper …