Results 81 to 90 of about 5,559,251 (271)

Modelling the operation of multireservoir systems using decomposition and stochastic dynamic programming [PDF]

, 2006
Stochastic dynamic programming models are attractive for multireservoir control problems because they allow non-linear features to be incorporated and changes in hydrological conditions to be modeled as Markov processes.
Archibald, Thomas W.; id_orcid, Archibald, T.W., McKinnon, K.I.M., McKinnon, K. I. M., Thomas, L.C., Thomas, L. C.   +5 more
core   +1 more source

Analysing the significance of small conformational changes and low occupancy states in serial crystallographic data

FEBS Open Bio, EarlyView.
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill, Yelyzaveta Pulnova, Elke De Zitter, Helen M. Ginn, Briony A. Yorke   +4 more
wiley   +1 more source

Data-Driven Pulsatile Blood Flow Physics with Dynamic Mode Decomposition

Fluids, 2020
Dynamic mode decomposition (DMD) is a purely data-driven and equation-free technique for reduced-order modeling of dynamical systems and fluid flow. DMD finds a best fit linear reduced-order model that represents any given spatiotemporal data.
Milad Habibi, Scott T. M. Dawson, Amirhossein Arzani   +2 more
doaj   +1 more source

Dynamic Mode Decomposition with Control Liouville Operators

IFAC-PapersOnLine, 2021
This paper builds the theoretical foundations for dynamic mode decomposition (DMD) of control-affine dynamical systems by leveraging the theory of vector-valued reproducing kernel Hilbert spaces (RKHSs). Specifically, control Liouville operators and control occupation kernels are introduced to separate the drift dynamics from the input dynamics.
Joel A. Rosenfeld, Rushikesh Kamalapurkar   +1 more
openaire   +4 more sources

Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif, Diane Demailly, Gabriella A. Horvàth, Juan Dario Ortigoza Escobar, Nathalie Dorison, Mayté Castro Jiménez, Cécile A. Hubsch, Thomas Wirth, Gun‐Marie Hariz, Sophie Huby, Morgan Dornadic, Zohra Souei, Muhammad Mushhood Ur Rehman, Simone Hemm, Mehdi Boulaymen, Eduardo M. Moraud, Jocelyne Bloch, Xavier Vasques   +17 more
wiley   +1 more source

On the convergence of Hermitian Dynamic Mode Decomposition

Physica D: Nonlinear Phenomena
24 pages, 4 figures.
Nicolas Boullé, Matthew J. Colbrook
openaire   +4 more sources

Deep learning enhanced dynamic mode decomposition

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2022
Koopman operator theory shows how nonlinear dynamical systems can be represented as an infinite-dimensional, linear operator acting on a Hilbert space of observables of the system. However, determining the relevant modes and eigenvalues of this infinite-dimensional operator can be difficult.
D. J. Alford-Lago, C. W. Curtis, A. T. Ihler, O. Issan   +3 more
openaire   +5 more sources

Natural Frequencies of Levodopa‐Induced Dyskinesia in Parkinson's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives Abnormal involuntary movements, known as dyskinesias, are common complications of levodopa treatment in patients with Parkinson's disease and can significantly impair quality of life. The underlying pathophysiology remains unclear, and current therapeutic options are limited.
Ioannis U. Isaias, Federica Avantaggiato, Gianni Pezzoli, Andrea Canessa   +3 more
wiley   +1 more source

Efficient Streaming Dynamic Mode Decomposition

IEEE Control Systems Letters
We propose a reformulation of the streaming dynamic mode decomposition method that requires maintaining a single orthonormal basis, thereby reducing computational redundancy. The proposed efficient streaming dynamic mode decomposition method results in a constant-factor reduction in computational complexity and memory storage requirements.
Aditya Kale, Marcos Netto, Xinyang Zhou
openaire   +4 more sources

The Multiverse of Dynamic Mode Decomposition Algorithms

CoRR, 2023
review article, 88 pages, 28 figures,
openaire   +3 more sources