Ioannis G. Kevrekidis

research

∙ 05/25/2023

Transporting Densities Across Dimensions

Even the best scientific equipment can only partially observe reality. R...

0 Michael Plainer, et al. ∙

research

∙ 05/05/2023

Data-driven and Physics Informed Modelling of Chinese Hamster Ovary Cell Bioreactors

Fed-batch culture is an established operation mode for the production of...

0 Tianqi Cui, et al. ∙

research

∙ 04/27/2023

Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information

Experimental data is often comprised of variables measured independently...

0 Saurabh Malani, et al. ∙

research

∙ 03/31/2023

Implementation and (Inverse Modified) Error Analysis for implicitly-templated ODE-nets

We focus on learning unknown dynamics from data using ODE-nets templated...

0 Aiqing Zhu, et al. ∙

research

∙ 03/15/2023

Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning

We present a physics-informed machine learning (PIML) scheme for the fee...

0 Hector Vargas Alvarez, et al. ∙

research

∙ 02/17/2023

On Equivalent Optimization of Machine Learning Methods

At the core of many machine learning methods resides an iterative optimi...

0 William T Redman, et al. ∙

research

∙ 02/09/2023

Gentlest ascent dynamics on manifolds defined by adaptively sampled point-clouds

Finding saddle points of dynamical systems is an important problem in pr...

0 Juan M. Bello-Rivas, et al. ∙

research

∙ 01/29/2023

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

Numerical simulations of multiphase flows are crucial in numerous engine...

0 Cristina P. Martin-Linares, et al. ∙

research

∙ 01/27/2023

Certified Invertibility in Neural Networks via Mixed-Integer Programming

Neural networks are notoriously vulnerable to adversarial attacks – smal...

0 Tianqi Cui, et al. ∙

research

∙ 01/27/2023

From partial data to out-of-sample parameter and observation estimation with Diffusion Maps and Geometric Harmonics

A data-driven framework is presented, that enables the prediction of qua...

0 Eleni D. Koronaki, et al. ∙

research

∙ 11/22/2022

A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms

Meta-learning of numerical algorithms for a given task consist of the da...

0 Danimir T. Doncevic, et al. ∙

research

∙ 10/28/2022

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

Many multiscale wave systems exhibit macroscale emergent behaviour, for ...

0 J. Divahar, et al. ∙

research

∙ 09/14/2022

Algorithmic (Semi-)Conjugacy via Koopman Operator Theory

Iterative algorithms are of utmost importance in decision and control. W...

0 William T Redman, et al. ∙

research

∙ 08/23/2022

Limits of Entrainment of Circadian Neuronal Networks

Circadian rhythmicity lies at the center of various important physiologi...

0 Yorgos M. Psarellis, et al. ∙

research

∙ 08/23/2022

GANs and Closures: Micro-Macro Consistency in Multiscale Modeling

Sampling the phase space of molecular systems – and, more generally, of ...

0 Ellis R. Crabtree, et al. ∙

research

∙ 07/26/2022

Staggered grids for multidimensional multiscale modelling

Numerical schemes for wave-like systems with small dissipation are often...

0 J. Divahar, et al. ∙

research

∙ 07/12/2022

Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approach

We present an Equation/Variable free machine learning (EVFML) framework ...

0 Dimitrios G. Patsatzis, et al. ∙

research

∙ 07/08/2022

Black and Gray Box Learning of Amplitude Equations: Application to Phase Field Systems

We present a data-driven approach to learning surrogate models for ampli...

0 Felix P. Kemeth, et al. ∙

research

∙ 05/26/2022

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

We propose a machine learning framework for the data-driven discovery of...

0 Seungjoon Lee, et al. ∙

research

∙ 04/26/2022

Double Diffusion Maps and their Latent Harmonics for Scientific Computations in Latent Space

We introduce a data-driven approach to building reduced dynamical models...

0 Nikolaos Evangelou, et al. ∙

research

∙ 04/21/2022

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

We introduce a method to successively locate equilibria (steady states) ...

0 Juan M. Bello-Rivas, et al. ∙

research

∙ 10/28/2021

An Operator Theoretic Perspective on Pruning Deep Neural Networks

The discovery of sparse subnetworks that are able to perform as well as ...

6 William T Redman, et al. ∙

research

∙ 10/13/2021

On the Parameter Combinations That Matter and on Those That do Not

We present a data-driven approach to characterizing nonidentifiability o...

0 Nikolaos Evangelou, et al. ∙

research

∙ 10/05/2021

On the Correspondence between Gaussian Processes and Geometric Harmonics

We discuss the correspondence between Gaussian process regression and Ge...

0 Felix Dietrich, et al. ∙

research

∙ 07/29/2021

Learning the temporal evolution of multivariate densities via normalizing flows

In this work, we propose a method to learn probability distributions usi...

5 Yubin Lu, et al. ∙

research

∙ 05/04/2021

Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators

We study the meta-learning of numerical algorithms for scientific comput...

0 Yue Guo, et al. ∙

research

∙ 04/27/2021

Initializing LSTM internal states via manifold learning

We present an approach, based on learning an intrinsic data manifold, fo...

0 Felix P. Kemeth, et al. ∙

research

∙ 03/13/2021

Helmholtzian Eigenmap: Topological feature discovery edge flow learning from point cloud data

The manifold Helmholtzian (1-Laplacian) operator Δ_1 elegantly generaliz...

0 Yu-Chia Chen, et al. ∙

research

∙ 12/23/2020

Learning emergent PDEs in a learned emergent space

We extract data-driven, intrinsic spatial coordinates from observations ...

93 Felix P. Kemeth, et al. ∙

research

∙ 12/05/2020

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 Pengzhan Jin, et al. ∙

research

∙ 08/24/2020

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

The data-driven discovery of partial differential equations (PDEs) consi...

0 Hassan Arbabi, et al. ∙

research

∙ 07/10/2020

Transformations between deep neural networks

We propose to test, and when possible establish, an equivalence between ...

0 Tom Bertalan, et al. ∙

research

∙ 04/15/2020

LOCA: LOcal Conformal Autoencoder for standardized data coordinates

We propose a deep-learning based method for obtaining standardized data ...

10 Erez Peterfreund, et al. ∙

research

∙ 04/13/2020

Emergent spaces for coupled oscillators

In this paper we present a systematic, data-driven approach to discoveri...

0 Thomas N. Thiem, et al. ∙

research

∙ 04/09/2020

Spectral Discovery of Jointly Smooth Features for Multimodal Data

In this paper, we propose a spectral method for deriving functions that ...

0 Or Yair, et al. ∙

research

∙ 12/17/2019

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...

0 J. E. Bunder, et al. ∙

research

∙ 09/12/2019

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

Complex spatiotemporal dynamics of physicochemical processes are often m...

10 Seungjoon Lee, et al. ∙

research

∙ 07/25/2019

On the Koopman operator of algorithms

A systematic mathematical framework for the study of numerical algorithm...

0 Felix Dietrich, et al. ∙

research

∙ 07/18/2019

A geometric approach to the transport of discontinuous densities

Different observations of a relation between inputs ("sources") and outp...

0 Caroline Moosmüller, et al. ∙

research

∙ 06/03/2019

Optimal Transport on the Manifold of SPD Matrices for Domain Adaptation

The problem of domain adaptation has become central in many applications...

0 Or Yair, et al. ∙

research

∙ 01/31/2019

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...

0 Simon L. Cotter, et al. ∙

research

∙ 12/16/2018

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

In statistical modeling with Gaussian Process regression, it has been sh...

0 Seungjoon Lee, et al. ∙

Ioannis G. Kevrekidis

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