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A classification scheme for chimera states

Chaos (Woodbury, N.Y.), 2016-09, Vol.26 (9)

2016

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From Discrete to Continuum Models of Three-Dimensional Deformations in Epithelial Sheets

Biophysical journal, 2015-07, Vol.109 (1), p.154-163

2015

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Shape Transformations of Epithelial Shells

Biophysical journal, 2016-04, Vol.110 (7), p.1670-1678

2016

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Systematic characterization of protein folding pathways using diffusion maps: application to Trp-cage miniprotein

The Journal of chemical physics, 2015-02, Vol.142 (8), p.085101-085101

2015

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Limits of entrainment of circadian neuronal networks

Chaos (Woodbury, N.Y.), 2023-01, Vol.33 (1), p.013137

2023

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On learning Hamiltonian systems from data

Chaos (Woodbury, N.Y.), 2019-12, Vol.29 (12)

2019

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Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator

Chaos (Woodbury, N.Y.), 2017-10, Vol.27 (10)

2017

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A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition

Journal of nonlinear science, 2015-12, Vol.25 (6), p.1307-1346

2015

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Dissipative particle dynamics simulations of polymer-protected nanoparticle self-assembly

The Journal of chemical physics, 2011-11, Vol.135 (18), p.184903-184903-10

2011

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Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

The Journal of chemical physics, 2014-09, Vol.141 (11), p.114102-114102

2014

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Transformations establishing equivalence across neural networks: When have two networks learned the same task?

Chaos (Woodbury, N.Y.), 2024-07, Vol.34 (7)

2024

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Bacterial population solitary waves can defeat rings of funnels

New journal of physics, 2017-03, Vol.19 (3), p.35002

2017

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Coarse-scale PDEs from fine-scale observations via machine learning

Chaos (Woodbury, N.Y.), 2020-01, Vol.30 (1)

2020

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Particles to partial differential equations parsimoniously

Chaos (Woodbury, N.Y.), 2021-03, Vol.31 (3)

2021

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Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

Proceedings of the National Academy of Sciences - PNAS, 2017-07, Vol.114 (28), p.E5494-E5503

2017

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Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

Chaos (Woodbury, N.Y.), 2022-08, Vol.32 (8)

2022

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Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning

Chaos (Woodbury, N.Y.), 2023-02, Vol.33 (2)

2023

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Manifold learning for parameter reduction

Journal of computational physics, 2019-09, Vol.392, p.419-431

2019

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Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data

Journal of fluid mechanics, 2023-11, Vol.975, Article A41

2023

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Learning the temporal evolution of multivariate densities via normalizing flows

Chaos (Woodbury, N.Y.), 2022-03, Vol.32 (3)

2022

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Nonlinear dimensionality reduction in molecular simulation: The diffusion map approach

Chemical physics letters, 2011-06, Vol.509 (1), p.1-11

2011

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Coarse nonlinear dynamics and metastability of filling-emptying transitions : Water in carbon nanotubes

Physical review letters, 2005-09, Vol.95 (13), p.130603.1-130603.4, Article 130603

2005

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Chimeras in random non-complete networks of phase oscillators

Chaos (Woodbury, N.Y.), 2012-03, Vol.22 (1), p.013132-013132-9

2012

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A general CFD framework for fault-resilient simulations based on multi-resolution information fusion

Journal of computational physics, 2017-10, Vol.347, p.290-304

2017

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Spatiotemporal Addressing of Surface Activity

Science (American Association for the Advancement of Science), 2001-10, Vol.294 (5540), p.134-137

2001

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A classification scheme for chimera states

From Discrete to Continuum Models of Three-Dimensional Deformations in Epithelial Sheets

Shape Transformations of Epithelial Shells

Systematic characterization of protein folding pathways using diffusion maps: application to Trp-cage miniprotein

Limits of entrainment of circadian neuronal networks

On learning Hamiltonian systems from data

Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator

A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition

Dissipative particle dynamics simulations of polymer-protected nanoparticle self-assembly

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

Transformations establishing equivalence across neural networks: When have two networks learned the same task?

Bacterial population solitary waves can defeat rings of funnels

Coarse-scale PDEs from fine-scale observations via machine learning

Particles to partial differential equations parsimoniously

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning

Manifold learning for parameter reduction

Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data

Learning the temporal evolution of multivariate densities via normalizing flows

Nonlinear dimensionality reduction in molecular simulation: The diffusion map approach

Coarse nonlinear dynamics and metastability of filling-emptying transitions : Water in carbon nanotubes

Chimeras in random non-complete networks of phase oscillators

A general CFD framework for fault-resilient simulations based on multi-resolution information fusion

Spatiotemporal Addressing of Surface Activity

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