DISCRETE- vs. CONTINUOUS-TIME NONLINEAR SIGNAL PROCESSING OF Cu ELECTRODISSOLUTION DATA

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Theory of the backpropagation neural network

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Improvement of the Empiricism in the BACK Equation of State via Hybrid Neural Networks

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Deep Ritz - Finite element methods: Neural network methods trained with finite elements

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Learning effective SDEs from Brownian dynamic simulations of colloidal particles

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

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THE KINETICS OF CRYSTALLIZATION OF TRIPALMITIN IN OLIVE OIL: AN ARTIFICIAL NEURAL NETWORK APPROACH

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Maintenance of transient chaos using a neural-network-assisted feedback control

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Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and OpportunitiesPreprint

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Forward and inverse problems of quasi-integrable Hamiltonian system using stochastic averaging method and physics-informed neural networks

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Fredholm Neural Networks

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Fast-slow neural networks for learning singularly perturbed dynamical systems

Serino, D. A., Loya, A. A., Burby, J. W., Kevrekidis, I. G., & Tang, Q. (2025) · Journal of Computational Physics · doi:10.1016/j.jcp.2025.114090 · Cited by 4

Runge–Kutta physics informed neural networks: formulation and analysis

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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions

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Learning emergent PDEs in a learned emergent spacePreprint

Kemeth, F. P., Bertalan, T., Thiem, T. N., Dietrich, F., Moon, S. J., Laing, C. R., & Kevrekidis, I. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12738 · Cited by 4

Tipping points of evolving epidemiological networks: Machine learning-assisted, data-driven effective modeling

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Differential Equations for Continuous-Time Deep Learning

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Dynamical System Analysis and Forecasting of Deformation Produced by an Earthquake Fault

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Implementation and (Inverse Modified) Error Analysis for Implicitly Templated ODE-Nets

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System Identification for Hybrid Systems using Neural NetworksPreprint

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PDE-free mass-constrained learning of complex systems with hidden states

Viola, G., Pia, A. D., Kevrekidis, I. G., Russo, L., & Siettos, C. I. (2026) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2026.118891 · Cited by 2

Kernel methods for some transport equations with application to learning kernels for the approximation of Koopman eigenfunctions: A unified approach via variational methods, Green’s functions and the method of characteristics

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

Guo, Y. P., Dietrich, F., Bertalan, T., Doncevic, D. T., Dahmen, M., Kevrekidis, I. G., & Li, Q. (2021) · arXiv (Cornell University) · Cited by 2

Data-driven identification of macroscopic dynamics with implicit equation-free sampling and Gaussian process regression: for the example of an integrate-and-fire neural network

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Prediction of the outlet temperature of an experimental heat exchanger using artificial neural networks

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Neural Gray-Box Identification of Nonlinear Partial Differential Equations

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Extending Microservice Model Validity using Universal Differential Equations

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Machine learning for the identification of phase transitions in interacting agent-based systems: A Desai-Zwanzig example

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From disorganized data to emergent dynamic models: Questionnaires to partial differential equations

Sroczynski, D. W., Kemeth, F. P., Georgiou, A., Coifman, R. R., & Kevrekidis, I. G. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf018 · Cited by 1

Process Forecasting, Modeling, and Control of Time-Dependent Systems

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A Remarkable Story

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OSCILLATORY BEHAVIOR AS ELECTROCHEMICAL FLUCTUATIONS DURING THE TRANSPASSIVE ELECTRODISSOLUTION OF COPPER IN PHOSPHORIC ACID

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Nonlinear Dimensionality Reduction then and Now: Aims for Dissipative Pdes in the Ml EraPreprint

Koronaki, E. D., Evangelou, N., Martin-Linares, C. P., Titi, E. S., & Kevrekidis, I. G. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4614194 · Cited by 1

The crystallization prediction of tripalmitin in triolein: an artificial neural network (ann) approach

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8 NONLINEAR TIME-SERIES ANALYSIS

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Introduction to Focus Issue: In Memory of John L. Hudson: Self-Organized Structures in Chemical Systems

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Modeling a recurrent, hidden dynamical system using energy minimization and kernel density estimates

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Partial observations and conservation laws: Grey-box modeling in biotechnology and optogeneticsPreprint

Lovelett, R. J., Avalos‬, J. L., & Kevrekidis, I. G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.04234

Statistical monitoring and fault diagnosing of multivariate processes based on nonlinear principal component analysis

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From Clutter to Clarity: Emergent Neural Operators via Questionnaire Metrics

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Efficient Manifold-Constrained Neural ODE for High-Dimensional Datasets

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Deep Learning for Partially Observed Dynamical Systems

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Machine Learning for Dynamical Systems Modelling

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Observations on Recurrent Loss in the Neural Network Model of a Partial Differential Equation: The Advection–Diffusion Equation

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Discrete- vs. Continuous-Time Nonlinear Signal Processing: Attractors, Transitions and Parallel Implementation Issues

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Application of neural nets to system identification and bifurcation analysis of real world experimental data

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Characterization of a two-parameter mixed-mode electrochemical behavior regime using neural networks

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Noninvertibility and resonance in discrete-time neural networks for time-series processing

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Controlling Chaos with Artificial Neural Network: Numerical Studies and Experiments

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