Predicting catastrophes in nonlinear dynamical systems by compressive sensing.
about
Data-based reconstruction of complex geospatial networks, nodal positioning and detection of hidden nodesLocating the source of diffusion in complex networks by time-reversal backward spreading.Multi-frequency complex network from time series for uncovering oil-water flow structure.Forecasting synchronizability of complex networks from data.Discovering governing equations from data by sparse identification of nonlinear dynamical systems.Reconstructing propagation networks with natural diversity and identifying hidden sources.Inferring network connectivity by delayed feedback control.Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression.Investigation on Law and Economics Based on Complex Network and Time Series Analysis.Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control.Investigation on law and economics of listed companies' financing preference based on complex network theoryUniversal data-based method for reconstructing complex networks with binary-state dynamics.Data-driven discovery of partial differential equations.Inferring sparse networks for noisy transient processesIdentifying structures of continuously-varying weighted networksComplex network analysis of phase dynamics underlying oil-water two-phase flows.Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso InitializationReconstruction of noise-driven nonlinear networks from node outputs by using high-order correlations.Optimal localization of diffusion sources in complex networksRevealing physical interaction networks from statistics of collective dynamicsRoles of mixing patterns in the network reconstruction.Bayesian inversion analysis of nonlinear dynamics in surface heterogeneous reactions.Recovering network topologies via Taylor expansion and compressive sensing.Prediction of invasion from the early stage of an epidemic.Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow.Identification of interactions in fractional-order systems with high dimensions.Identifying dynamical systems with bifurcations from noisy partial observation.Model-free inference of direct network interactions from nonlinear collective dynamics.Multivariate weighted recurrence network inference for uncovering oil-water transitional flow behavior in a vertical pipe.Reconstructing complex networks without time series.Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG.Directed weighted network structure analysis of complex impedance measurements for characterizing oil-in-water bubbly flow.Network dynamics for optimal compressive-sensing input-signal recovery.Detecting hidden nodes in complex networks from time series.Escaping the curse of dimensionality in estimating multivariate transfer entropy.Model selection for dynamical systems via sparse regression and information criteria.Three-Dimensional Filamentation Analysis of SDSS DR5 Survey
P2860
Q28601521-125C8380-D817-4ED8-950F-4C9807D4C7AFQ30386881-5BA6D1A9-FAEA-46A3-84B6-51112102DCEDQ30419023-FCCF227F-57BF-417C-86DB-3DF4DEB5DEFBQ30570342-54A1AED7-1E17-4396-9AB4-334617F2E098Q31066590-291630EF-6409-409C-B073-451E17D03FECQ33924080-0CB00ACE-3157-4980-9254-8452C5D05159Q34039429-2EDC0DEA-583E-4980-91E8-CA52A96BC6A4Q35585965-9E2891B9-D0DB-4CAE-8E2E-F2A438D811D4Q35664239-90016230-8557-49F5-8700-99B84831B4A0Q35937613-045135A4-0166-4707-B81E-E5C99BC0CD3BQ36310544-73F972B9-6CB8-4FD7-8CC0-C56C4B2405D5Q36348722-065CE48D-4D2B-424D-A493-D99A49771DCBQ36372738-1589AE6D-9AAC-4027-8A08-17C6B0E75A3EQ36621409-6108CAF4-87FA-4132-A20B-E9C42AD6D058Q36990417-3646411E-3261-4C6A-9923-15E9CC26861DQ37010650-70640743-822B-4C3D-8A21-E1062E1A0E21Q37439137-3BD16A34-4BE6-433A-8FCA-1755FA7A6472Q37712930-358C062F-4E00-41B5-97A1-D37736F11338Q38796722-88B30100-4E1A-482D-812D-39B589F36679Q38936631-C28C04E9-31B0-44A9-BF49-D69409A198DCQ39102216-FB1E5463-AFDE-48BA-9E91-5AC493826B4BQ39292374-6A189BC6-8BC0-496A-9CA7-9603AB4F945CQ40987079-C610A746-649B-4D11-A32B-11D8011F73DBQ42156136-9E655779-7978-4BCE-8C84-E46081FB16F7Q43495448-B5C530C9-A476-4860-B217-68C9541E2679Q43726576-166C0C73-6B5B-4FFA-8F61-9F0EC6FE2E17Q45300249-F2C25D0F-960B-43BB-9263-B5FE7E9AEB39Q47111496-9A494A67-22C1-4E29-B97F-5D61C4BC21A4Q47415897-751B60A2-6948-446A-B6C0-1707A5B9D86FQ47629638-12CC63CC-CFC9-47E7-98EA-5AF4D18C99C0Q48434196-983147BA-F637-4895-BDB7-E2C1BEB766E4Q50937030-AD949DFB-6F7D-4E78-9133-63AE8DF2E502Q51015713-17519EF1-BDFC-446E-A9CA-18B9FC03C835Q51319679-FE58B461-DE6D-4F27-8B18-8B4DEBB5C1B6Q51320190-A998ECE3-6C28-4671-A56A-8E15873FBFBDQ51561361-31B9AC21-9BB3-4776-822B-6411DD3D0DB4Q58689963-7ED491F1-73FB-49BE-A020-06BB80C2C749
P2860
Predicting catastrophes in nonlinear dynamical systems by compressive sensing.
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年学术文章
@wuu
2011年学术文章
@zh
2011年学术文章
@zh-cn
2011年学术文章
@zh-hans
2011年学术文章
@zh-my
2011年学术文章
@zh-sg
2011年學術文章
@yue
2011年學術文章
@zh-hant
name
Predicting catastrophes in nonlinear dynamical systems by compressive sensing.
@en
Predicting catastrophes in nonlinear dynamical systems by compressive sensing.
@nl
type
label
Predicting catastrophes in nonlinear dynamical systems by compressive sensing.
@en
Predicting catastrophes in nonlinear dynamical systems by compressive sensing.
@nl
prefLabel
Predicting catastrophes in nonlinear dynamical systems by compressive sensing.
@en
Predicting catastrophes in nonlinear dynamical systems by compressive sensing.
@nl
P2093
P2860
P1476
Predicting catastrophes in nonlinear dynamical systems by compressive sensing.
@en
P2093
Vassilios Kovanis
Wen-Xu Wang
P2860
P304
P356
10.1103/PHYSREVLETT.106.154101
P407
P577
2011-04-15T00:00:00Z