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Felix Dietrich

Showing results (1-10 of 18) with videos related to

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Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|July 15, 2014
Gradient navigation model for pedestrian dynamicsFelix Dietrich, Gerta Köster
Chaos (Woodbury, N.Y.)|July 18, 2024
Transformations establishing equivalence across neural networks: When have two networks learned the same task?Tom Bertalan, Felix Dietrich, Ioannis G Kevrekidis
Chaos (Woodbury, N.Y.)|January 29, 2024
Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operatorIoannis K Gallos, Daniel Lehmberg, Felix Dietrich, et al.
Chaos (Woodbury, N.Y.)|January 3, 2020
On learning Hamiltonian systems from dataTom Bertalan, Felix Dietrich, Igor Mezić, et al.
Interface Focus|May 9, 2019
Linking Gaussian process regression with data-driven manifold embeddings for nonlinear data fusionSeungjoon Lee, Felix Dietrich, George E Karniadakis, et al.
Chaos (Woodbury, N.Y.)|November 3, 2017
Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operatorQianxiao Li, Felix Dietrich, Erik M Bollt, et al.
Chaos (Woodbury, N.Y.)|May 3, 2020
Manifold learning for organizing unstructured sets of process observationsFelix Dietrich, Mahdi Kooshkbaghi, Erik M Bollt, et al.
Elife|October 19, 2013
Inter-Golgi transport mediated by COPI-containing vesicles carrying small cargoesPatrina A Pellett, Felix Dietrich, Jörg Bewersdorf, et al.
Chaos (Woodbury, N.Y.)|April 2, 2022
Learning the temporal evolution of multivariate densities via normalizing flowsYubin Lu, Romit Maulik, Ting Gao, et al.
Chaos (Woodbury, N.Y.)|March 1, 2023
Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learningFelix Dietrich, Alexei Makeev, George Kevrekidis, et al.
Pageof 2

Showing results (1-10 of 18) with videos related to

Sort By:
Pageof 2
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|July 15, 2014
Gradient navigation model for pedestrian dynamicsFelix Dietrich, Gerta Köster
Chaos (Woodbury, N.Y.)|July 18, 2024
Transformations establishing equivalence across neural networks: When have two networks learned the same task?Tom Bertalan, Felix Dietrich, Ioannis G Kevrekidis
Chaos (Woodbury, N.Y.)|January 29, 2024
Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operatorIoannis K Gallos, Daniel Lehmberg, Felix Dietrich, et al.
Chaos (Woodbury, N.Y.)|January 3, 2020
On learning Hamiltonian systems from dataTom Bertalan, Felix Dietrich, Igor Mezić, et al.
Interface Focus|May 9, 2019
Linking Gaussian process regression with data-driven manifold embeddings for nonlinear data fusionSeungjoon Lee, Felix Dietrich, George E Karniadakis, et al.
Chaos (Woodbury, N.Y.)|November 3, 2017
Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operatorQianxiao Li, Felix Dietrich, Erik M Bollt, et al.
Chaos (Woodbury, N.Y.)|May 3, 2020
Manifold learning for organizing unstructured sets of process observationsFelix Dietrich, Mahdi Kooshkbaghi, Erik M Bollt, et al.
Elife|October 19, 2013
Inter-Golgi transport mediated by COPI-containing vesicles carrying small cargoesPatrina A Pellett, Felix Dietrich, Jörg Bewersdorf, et al.
Chaos (Woodbury, N.Y.)|April 2, 2022
Learning the temporal evolution of multivariate densities via normalizing flowsYubin Lu, Romit Maulik, Ting Gao, et al.
Chaos (Woodbury, N.Y.)|March 1, 2023
Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learningFelix Dietrich, Alexei Makeev, George Kevrekidis, et al.
Pageof 2