Ioannis G. Kevrekidis

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Learning emergent PDEs in a learned emergent space

We extract data-driven, intrinsic spatial coordinates from observations ...
12/23/2020 ∙ by Felix P. Kemeth, et al. ∙ 93 ∙ share

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Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks

We propose the Poisson neural networks (PNNs) to learn Poisson systems a...
12/05/2020 ∙ by Pengzhan Jin, et al. ∙ 12 ∙ share

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Linking Machine Learning with Multiscale Numerics: Data-Driven Discovery of Homogenized Equations

The data-driven discovery of partial differential equations (PDEs) consi...
08/24/2020 ∙ by Hassan Arbabi, et al. ∙ 0 ∙ share

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Transformations between deep neural networks

We propose to test, and when possible establish, an equivalence between ...
07/10/2020 ∙ by Tom Bertalan, et al. ∙ 0 ∙ share

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LOCA: LOcal Conformal Autoencoder for standardized data coordinates

We propose a deep-learning based method for obtaining standardized data ...
04/15/2020 ∙ by Erez Peterfreund, et al. ∙ 10 ∙ share

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Emergent spaces for coupled oscillators

In this paper we present a systematic, data-driven approach to discoveri...
04/13/2020 ∙ by Thomas N. Thiem, et al. ∙ 0 ∙ share

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Spectral Discovery of Jointly Smooth Features for Multimodal Data

In this paper, we propose a spectral method for deriving functions that ...
04/09/2020 ∙ by Or Yair, et al. ∙ 0 ∙ share

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Large-scale simulation of shallow water waves with computation only on small staggered patches

The multiscale patch scheme is built from given small micro-scale simula...
12/17/2019 ∙ by J. E. Bunder, et al. ∙ 0 ∙ share

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

Complex spatiotemporal dynamics of physicochemical processes are often m...
09/12/2019 ∙ by Seungjoon Lee, et al. ∙ 10 ∙ share

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On the Koopman operator of algorithms

A systematic mathematical framework for the study of numerical algorithm...
07/25/2019 ∙ by Felix Dietrich, et al. ∙ 0 ∙ share

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A geometric approach to the transport of discontinuous densities

Different observations of a relation between inputs ("sources") and outp...
07/18/2019 ∙ by Caroline Moosmüller, et al. ∙ 0 ∙ share

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Optimal Transport on the Manifold of SPD Matrices for Domain Adaptation

The problem of domain adaptation has become central in many applications...
06/03/2019 ∙ by Or Yair, et al. ∙ 0 ∙ share

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Transport map accelerated adaptive importance sampling, and application to inverse problems arising from multiscale stochastic reaction networks

In many applications, Bayesian inverse problems can give rise to probabi...
01/31/2019 ∙ by Simon L. Cotter, et al. ∙ 0 ∙ share

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Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion

In statistical modeling with Gaussian Process regression, it has been sh...
12/16/2018 ∙ by Seungjoon Lee, et al. ∙ 0 ∙ share

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Ioannis G. Kevrekidis

Featured Co-authors

George Em Karniadakis

Zhen Zhang

Ronen Talmon

Ofir Lindenbaum

Felix Dietrich

Ronald R. Coifman

Matan Gavish

Pengzhan Jin

Giovanni Samaey

Tom Bertalan

A. J. Roberts

Learning emergent PDEs in a learned emergent space

Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks

Linking Machine Learning with Multiscale Numerics: Data-Driven Discovery of Homogenized Equations

Transformations between deep neural networks

LOCA: LOcal Conformal Autoencoder for standardized data coordinates

Emergent spaces for coupled oscillators

Spectral Discovery of Jointly Smooth Features for Multimodal Data

Large-scale simulation of shallow water waves with computation only on small staggered patches

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

On the Koopman operator of algorithms

A geometric approach to the transport of discontinuous densities

Optimal Transport on the Manifold of SPD Matrices for Domain Adaptation

Transport map accelerated adaptive importance sampling, and application to inverse problems arising from multiscale stochastic reaction networks

Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion