Ioannis G. Kevrekidis

Orcid: 0000-0003-2220-3522

Affiliations:
  • Johns Hopkins University, Chemical and Biomolecular Engineering, Baltimore, MD, USA
  • Princeton University, Department of Chemical Engineering


According to our database1, Ioannis G. Kevrekidis authored at least 157 papers between 1993 and 2024.

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Bibliography

2024
Machine Learning Memory Kernels as Closure for Non-Markovian Stochastic Processes.
IEEE Trans. Neural Networks Learn. Syst., May, 2024

A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms.
SIAM J. Sci. Comput., 2024

Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era.
J. Comput. Phys., 2024

Integrating supervised and unsupervised learning approaches to unveil critical process inputs.
CoRR, 2024

Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy.
CoRR, 2024

Nonlinear Discrete-Time Observers with Physics-Informed Neural Networks.
CoRR, 2024

Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification.
CoRR, 2024

2023
Learning Poisson Systems and Trajectories of Autonomous Systems via Poisson Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., November, 2023

Discrete-time nonlinear feedback linearization via physics-informed machine learning.
J. Comput. Phys., November, 2023

Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information.
Comput. Chem. Eng., October, 2023

From partial data to out-of-sample parameter and observation estimation with diffusion maps and geometric harmonics.
Comput. Chem. Eng., October, 2023

GANs and Closures: Micro-Macro Consistency in Multiscale Modeling.
Multiscale Model. Simul., September, 2023

Data-driven control of agent-based models: An Equation/Variable-free machine learning approach.
J. Comput. Phys., April, 2023

Double Diffusion Maps and their Latent Harmonics for scientific computations in latent space.
J. Comput. Phys., 2023

AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression.
CoRR, 2023

Gappy local conformal auto-encoders for heterogeneous data fusion: in praise of rigidity.
CoRR, 2023

Micro-Macro Consistency in Multiscale Modeling: Score-Based Model Assisted Sampling of Fast/Slow Dynamical Systems.
CoRR, 2023

Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling.
CoRR, 2023

Machine Learning for the identification of phase-transitions in interacting agent-based systems.
CoRR, 2023

Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points.
CoRR, 2023

On-Manifold Projected Gradient Descent.
CoRR, 2023

Data-driven and Physics Informed Modelling of Chinese Hamster Ovary Cell Bioreactors.
CoRR, 2023

Equation-Free Computations as DDDAS Protocols for Bifurcation Studies: A Granular Chain Example.
CoRR, 2023

Implementation and (Inverse Modified) Error Analysis for implicitly-templated ODE-nets.
CoRR, 2023

A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN).
CoRR, 2023

On Equivalent Optimization of Machine Learning Methods.
CoRR, 2023

Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points.
CoRR, 2023

Gentlest ascent dynamics on manifolds defined by adaptively sampled point-clouds.
CoRR, 2023

Accurate and efficient multiscale simulation of a heterogeneous elastic beam via computation on small sparse patches.
CoRR, 2023

Benchmarking optimality of time series classification methods in distinguishing diffusions.
CoRR, 2023

Physics-agnostic and Physics-infused machine learning for thin films flows: modeling, and predictions from small data.
CoRR, 2023

Certified Invertibility in Neural Networks via Mixed-Integer Programming.
Proceedings of the Learning for Dynamics and Control Conference, 2023

2022
Adaptively Detect and Accurately Resolve Macro-scale Shocks in an Efficient Equation-Free Multiscale Simulation.
SIAM J. Sci. Comput., August, 2022

Spectral Discovery of Jointly Smooth Features for Multimodal Data.
SIAM J. Math. Data Sci., 2022

Personalized Algorithm Generation: A Case Study in Learning ODE Integrators.
SIAM J. Sci. Comput., 2022

Numerical Bifurcation Analysis of PDEs From Lattice Boltzmann Model Simulations: a Parsimonious Machine Learning Approach.
J. Sci. Comput., 2022

Two novel families of multiscale staggered patch schemes efficiently simulate large-scale, weakly damped, linear waves.
CoRR, 2022

Limits of Entrainment of Circadian Neuronal Networks.
CoRR, 2022

Staggered grids for multidimensional multiscale modelling.
CoRR, 2022

Unsupervised learning of observation functions in state-space models by nonparametric moment methods.
CoRR, 2022

Black and Gray Box Learning of Amplitude Equations: Application to Phase Field Systems.
CoRR, 2022

Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data.
CoRR, 2022

Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal Particles.
CoRR, 2022

Staying the course: Locating equilibria of dynamical systems on Riemannian manifolds defined by point-clouds.
CoRR, 2022

Constructing coarse-scale bifurcation diagrams from spatio-temporal observations of microscopic simulations: A parsimonious machine learning approach.
CoRR, 2022

An Operator Theoretic View On Pruning Deep Neural Networks.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Algorithmic (Semi-)Conjugacy via Koopman Operator Theory.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2021
Equation-free patch scheme for efficient computational homogenisation via self-adjoint coupling.
Numerische Mathematik, 2021

An Operator Theoretic Perspective on Pruning Deep Neural Networks.
CoRR, 2021

On the Parameter Combinations That Matter and on Those That do Not.
CoRR, 2021

On the Correspondence between Gaussian Processes and Geometric Harmonics.
CoRR, 2021

Learning the temporal evolution of multivariate densities via normalizing flows.
CoRR, 2021

Learning effective stochastic differential equations from microscopic simulations: combining stochastic numerics and deep learning.
CoRR, 2021

Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators.
CoRR, 2021

Initializing LSTM internal states via manifold learning.
CoRR, 2021

Helmholtzian Eigenmap: Topological feature discovery & edge flow learning from point cloud data.
CoRR, 2021

Exploring critical points of energy landscapes: From low-dimensional examples to phase field crystal PDEs.
Commun. Nonlinear Sci. Numer. Simul., 2021

Coarse-grained and Emergent Distributed Parameter Systems from Data.
Proceedings of the 2021 American Control Conference, 2021

Applications of Koopman Mode Analysis to Neural Networks.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021

2020
On the Koopman Operator of Algorithms.
SIAM J. Appl. Dyn. Syst., 2020

A Geometric Approach to the Transport of Discontinuous Densities.
SIAM/ASA J. Uncertain. Quantification, 2020

Transport Map Accelerated Adaptive Importance Sampling, and Application to Inverse Problems Arising from Multiscale Stochastic Reaction Networks.
SIAM/ASA J. Uncertain. Quantification, 2020

Emergent Spaces for Coupled Oscillators.
Frontiers Comput. Neurosci., 2020

Learning emergent PDEs in a learned emergent space.
CoRR, 2020

Particles to Partial Differential Equations Parsimoniously.
CoRR, 2020

Linking Machine Learning with Multiscale Numerics: Data-Driven Discovery of Homogenized Equations.
CoRR, 2020

Transformations between deep neural networks.
CoRR, 2020

LOCA: LOcal Conformal Autoencoder for standardized data coordinates.
CoRR, 2020

2019
Correction to: Optimal deterministic algorithm generation.
J. Glob. Optim., 2019

Manifold learning for parameter reduction.
J. Comput. Phys., 2019

Designing networks with resiliency to edge failures using two-stage robust optimization.
Eur. J. Oper. Res., 2019

Large-scale simulation of shallow water waves with computation only on small staggered patches.
CoRR, 2019

Coarse-scale PDEs from fine-scale observations via machine learning.
CoRR, 2019

Optimal Transport on the Manifold of SPD Matrices for Domain Adaptation.
CoRR, 2019

2018
On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions.
SIAM J. Appl. Dyn. Syst., 2018

Data-Driven Model Reduction and Transfer Operator Approximation.
J. Nonlinear Sci., 2018

Optimal deterministic algorithm generation.
J. Glob. Optim., 2018

Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion.
CoRR, 2018

An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning.
IEEE Access, 2018

2017
Synthesizing developmental trajectories.
PLoS Comput. Biol., 2017

A general CFD framework for fault-resilient simulations based on multi-resolution information fusion.
J. Comput. Phys., 2017

A resilient and efficient CFD framework: Statistical learning tools for multi-fidelity and heterogeneous information fusion.
J. Comput. Phys., 2017

Good coupling for the multiscale patch scheme on systems with microscale heterogeneity.
J. Comput. Phys., 2017

Coarse-Grained Descriptions of Dynamics for Networks with Both Intrinsic and Structural Heterogeneities.
Frontiers Comput. Neurosci., 2017

An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients.
Entropy, 2017

2016
Accuracy of Patch Dynamics with Mesoscale Temporal Coupling for Efficient Massively Parallel Simulations.
SIAM J. Sci. Comput., 2016

Data-Driven Reduction for a Class of Multiscale Fast-Slow Stochastic Dynamical Systems.
SIAM J. Appl. Dyn. Syst., 2016

Coarse-Graining and Simplification of the Dynamics Seen in Bursting Neurons.
SIAM J. Appl. Dyn. Syst., 2016

Designing networks: A mixed-integer linear optimization approach.
Networks, 2016

No equations, no parameters, no variables: data, and the reconstruction of normal forms by learning informed observation geometries.
CoRR, 2016

Data mining when each data point is a network.
CoRR, 2016

2015
A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition.
J. Nonlinear Sci., 2015

Equation-free analysis of spike-timing-dependent plasticity.
Biol. Cybern., 2015

2014
Time varying radial basis functions.
J. Comput. Appl. Math., 2014

A hybrid stochastic-deterministic algorithm for lattice-gas models of catalytic reactions and the computation of TPD spectra.
Comput. Chem. Eng., 2014

2013
Simulating Stochastic Inertial Manifolds by a Backward-Forward Approach.
SIAM J. Appl. Dyn. Syst., 2013

Simple Urban Simulation Atop Complicated Models: Multi-Scale Equation-Free Computing of Sprawl Using Geographic Automata.
Entropy, 2013

Analysis of data in the form of graphs.
CoRR, 2013

Noisy dynamic simulations in the presence of symmetry: Data alignment and model reduction.
Comput. Math. Appl., 2013

State reduction in molecular simulations.
Comput. Chem. Eng., 2013

2012
Accelerating agent-based computation of complex urban systems.
Int. J. Geogr. Inf. Sci., 2012

An equation-free approach to coarse-graining the dynamics of networks
CoRR, 2012

Mathematical modeling and steady-state analysis of a co-ionic-conducting solid oxide fuel cell.
Proceedings of the American Control Conference, 2012

2011
Analysis and Computation of a Discrete KdV-Burgers Type Equation with Fast Dispersion and Slow Diffusion.
SIAM J. Numer. Anal., 2011

Generation of networks with prescribed degree-dependent clustering.
Optim. Lett., 2011

A common approach to the computation of coarse-scale steady states and to consistent initialization on a slow manifold.
Comput. Chem. Eng., 2011

2010
Equation-free modeling.
Scholarpedia, 2010

Coarse-grained computation for particle coagulation and sintering processes by linking Quadrature Method of Moments with Monte-Carlo.
J. Comput. Phys., 2010

Reduced models for binocular rivalry.
J. Comput. Neurosci., 2010

Equation-Free Multiscale Computations in Social Networks: from Agent-Based Modeling to Coarse-Grained Stability and bifurcation Analysis.
Int. J. Bifurc. Chaos, 2010

Bifurcations of lurching waves in a thalamic neuronal network.
Biol. Cybern., 2010

2009
Analysis of a Stochastic Chemical System Close to a SNIPER Bifurcation of Its Mean-Field Model.
SIAM J. Appl. Math., 2009

An Efficient Newton-Krylov Implementation of the Constrained Runs Scheme for Initializing on a Slow Manifold.
J. Sci. Comput., 2009

2008
Uncertainty Quantification for Atomistic Reaction Models: An Equation-Free Stochastic Simulation Algorithm Example.
Multiscale Model. Simul., 2008

Multiscale Integration Schemes for Jump-Diffusion Systems.
Multiscale Model. Simul., 2008

Diffusion Maps, Reduction Coordinates, and Low Dimensional Representation of Stochastic Systems.
Multiscale Model. Simul., 2008

2007
General Tooth Boundary Conditions for Equation Free Modeling.
SIAM J. Sci. Comput., 2007

Deciding the Nature of the Coarse Equation through Microscopic Simulations: The Baby-Bathwater Scheme.
SIAM Rev., 2007

Condition Estimates for Pseudo-Arclength Continuation.
SIAM J. Numer. Anal., 2007

Bistability and Oscillations in the Huang-Ferrell Model of MAPK Signaling.
PLoS Comput. Biol., 2007

Variance Reduction for the Equation-Free Simulation of Multiscale Stochastic Systems.
Multiscale Model. Simul., 2007

Template-Based Stabilization of Relative Equilibria in Systems with Continuous Symmetry.
J. Nonlinear Sci., 2007

Projective and coarse projective integration for problems with continuous symmetries.
J. Comput. Phys., 2007

A Computer-Assisted Study of Global Dynamic Transitions for a Noninvertible System.
Int. J. Bifurc. Chaos, 2007

2006
The Moment Map: Nonlinear Dynamics of Density Evolution via a Few Moments.
SIAM J. Appl. Dyn. Syst., 2006

Patch dynamics with buffers for homogenization problems.
J. Comput. Phys., 2006

An equation-Free Approach to Nonlinear Control: Coarse Feedback Linearization with Pole-Placement.
Int. J. Bifurc. Chaos, 2006

An equation-Free Approach to Coupled oscillator Dynamics: the Kuramoto Model Example.
Int. J. Bifurc. Chaos, 2006

A systems-based approach to multiscale computation: Equation-free detection of coarse-grained bifurcations.
Comput. Chem. Eng., 2006

Template-based stabilization of relative equilibria.
Proceedings of the American Control Conference, 2006

2005
Projecting to a Slow Manifold: Singularly Perturbed Systems and Legacy Codes.
SIAM J. Appl. Dyn. Syst., 2005

Equation-Free, Multiscale Computation for Unsteady Random Diffusion.
Multiscale Model. Simul., 2005

Application of Coarse Integration to Bacterial Chemotaxis.
Multiscale Model. Simul., 2005

The Gap-Tooth Scheme for Homogenization Problems.
Multiscale Model. Simul., 2005

Constraint-Defined Manifolds: a Legacy Code Approach to Low-Dimensional Computation.
J. Sci. Comput., 2005

Equation-Free, Effective Computation for Discrete Systems: a Time stepper Based Approach.
Int. J. Bifurc. Chaos, 2005

An equation-free, multiscale approach to uncertainty quantification.
Comput. Sci. Eng., 2005

Equation-free gaptooth-based controller design for distributed complex/multiscale processes.
Comput. Chem. Eng., 2005

Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Equation-free, coarse-grained feedback linearization.
Proceedings of the American Control Conference, 2005

Model-based control methodologies for catalytic surface reactions.
Proceedings of the American Control Conference, 2005

2004
Coarse bifurcation Diagrams via Microscopic Simulators: a State-Feedback Control-Based Approach.
Int. J. Bifurc. Chaos, 2004

Bifurcation analysis of nonlinear reaction-diffusion problems using wavelet-based reduction techniques.
Comput. Chem. Eng., 2004

Optimal sensor location and reduced order observer design for distributed process systems.
Comput. Chem. Eng., 2004

The gaptooth scheme, patch dynamics and equation-free controller design for distributed complex/multiscale processes.
Proceedings of the 2004 American Control Conference, 2004

2003
Projective Methods for Stiff Differential Equations: Problems with Gaps in Their Eigenvalue Spectrum.
SIAM J. Sci. Comput., 2003

Enabling stability analysis of tubular reactor models using PDE/PDAE integrators.
Comput. Chem. Eng., 2003

Optimal switching policies using coarse timesteppers.
Proceedings of the 42nd IEEE Conference on Decision and Control, 2003

Nonlinear order reduction of discretized cell population models.
Proceedings of the American Control Conference, 2003

Distributed nonlinear control of diffusion-reaction processes.
Proceedings of the American Control Conference, 2003

Time-steppers and coarse-grained control of microscopic distributed processes.
Proceedings of the American Control Conference, 2003

2001
The Oseberg Transition: Visualization of Global bifurcations for the Kuramoto-Sivashinsky equation.
Int. J. Bifurc. Chaos, 2001

1997
Unsteady Two-Dimensional Flows in Complex Geometries: Comparative Bifurcation Studies with Global Eigenfunction Expansions.
SIAM J. Sci. Comput., 1997

Two-dimensional invariant manifolds and global bifurcations: some approximation and visualization studies.
Numer. Algorithms, 1997

1993
Identification of Continuous-Time Dynamical Systems: Neural Network Based Algorithms and Parallel Implementation.
Proceedings of the Sixth SIAM Conference on Parallel Processing for Scientific Computing, 1993

Noninvertibility in neural networks.
Proceedings of International Conference on Neural Networks (ICNN'88), San Francisco, CA, USA, March 28, 1993

Continuous time modeling of nonlinear systems: a neural network-based approach.
Proceedings of International Conference on Neural Networks (ICNN'88), San Francisco, CA, USA, March 28, 1993


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