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Thomas Stemler

Showing results (11-20 of 27) with videos related to

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Chaos (Woodbury, N.Y.)|January 2, 2017
Counting forbidden patterns in irregularly sampled time series. II. Reliability in the presence of highly irregular samplingKonstantinos Sakellariou, Michael McCullough, Thomas Stemler, et al.
Chaos (Woodbury, N.Y.)|April 3, 2017
Regenerating time series from ordinal networksMichael McCullough, Konstantinos Sakellariou, Thomas Stemler, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|June 4, 2008
Noise-free stochastic resonance at an interior crisisThomas Jüngling, Hartmut Benner, Thomas Stemler, et al.
Chaos (Woodbury, N.Y.)|January 2, 2017
Counting forbidden patterns in irregularly sampled time series. I. The effects of under-sampling, random depletion, and timing jitterMichael McCullough, Konstantinos Sakellariou, Thomas Stemler, et al.
Chaos (Woodbury, N.Y.)|February 3, 2018
Prediction of flow dynamics using point processesYoshito Hirata, Thomas Stemler, Deniz Eroglu, et al.
Chaos (Woodbury, N.Y.)|January 1, 2022
Grading your models: Assessing dynamics learning of models using persistent homologyEugene Tan, Débora Corrêa, Thomas Stemler, et al.
Physical Review Letters|March 16, 2007
Stochastic modeling of experimental chaotic time seriesThomas Stemler, Johannes P Werner, Hartmut Benner, et al.
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|December 2, 2009
Stochastic modelling of intermittencyThomas Stemler, Johannes P Werner, Hartmut Benner, et al.
Chaos (Woodbury, N.Y.)|June 1, 2015
Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systemsMichael McCullough, Michael Small, Thomas Stemler, et al.
Chaos (Woodbury, N.Y.)|January 3, 2020
Learned emergence in selfish collective motionShannon D Algar, Thomas Lymburn, Thomas Stemler, et al.
Pageof 3

Showing results (11-20 of 27) with videos related to

Sort By:
Pageof 3
Chaos (Woodbury, N.Y.)|January 2, 2017
Counting forbidden patterns in irregularly sampled time series. II. Reliability in the presence of highly irregular samplingKonstantinos Sakellariou, Michael McCullough, Thomas Stemler, et al.
Chaos (Woodbury, N.Y.)|April 3, 2017
Regenerating time series from ordinal networksMichael McCullough, Konstantinos Sakellariou, Thomas Stemler, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|June 4, 2008
Noise-free stochastic resonance at an interior crisisThomas Jüngling, Hartmut Benner, Thomas Stemler, et al.
Chaos (Woodbury, N.Y.)|January 2, 2017
Counting forbidden patterns in irregularly sampled time series. I. The effects of under-sampling, random depletion, and timing jitterMichael McCullough, Konstantinos Sakellariou, Thomas Stemler, et al.
Chaos (Woodbury, N.Y.)|February 3, 2018
Prediction of flow dynamics using point processesYoshito Hirata, Thomas Stemler, Deniz Eroglu, et al.
Chaos (Woodbury, N.Y.)|January 1, 2022
Grading your models: Assessing dynamics learning of models using persistent homologyEugene Tan, Débora Corrêa, Thomas Stemler, et al.
Physical Review Letters|March 16, 2007
Stochastic modeling of experimental chaotic time seriesThomas Stemler, Johannes P Werner, Hartmut Benner, et al.
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|December 2, 2009
Stochastic modelling of intermittencyThomas Stemler, Johannes P Werner, Hartmut Benner, et al.
Chaos (Woodbury, N.Y.)|June 1, 2015
Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systemsMichael McCullough, Michael Small, Thomas Stemler, et al.
Chaos (Woodbury, N.Y.)|January 3, 2020
Learned emergence in selfish collective motionShannon D Algar, Thomas Lymburn, Thomas Stemler, et al.
Pageof 3