Tarek Echekki

Professor

North Carolina State University

United States

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Professor Tarek Echekki is a faculty member at North Carolina State University in the United States. His research focuses on advanced topics in combustion chemistry, including physics-constrained machine learning applications, turbulent combustion modeling, and chemical kinetics acceleration. His recent publications emphasize innovative frameworks and methodologies for enhancing the understanding and predictive capability of complex fuels and combustion processes.

Articles (18)

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2025

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2024

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2022

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2022

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2022

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2021

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2021

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2020

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2020

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2020

Collaborators (1)

M. Rafi Malik

Université libre de Bruxelles

BELGIUM

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Tarek Echekki

Research Interests

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Articles (18)

Physics-constrained machine learning for reduced composition space chemical kinetics

A PINN-DeepONet framework for extracting turbulent combustion closure from multiscalar measurements

Combustion chemistry acceleration with DeepONets

A Data-Based Hybrid Chemistry Acceleration Framework for the Low-Temperature Oxidation of Complex Fuels

On the application of principal component transport for compression ignition of lean fuel/air mixtures under engine relevant conditions

Comparisons of Different Representative Species Selection Schemes for Reduced-Order Modeling and Chemistry Acceleration of Complex Hydrocarbon Fuels

Transfer learning for predicting source terms of principal component transport in chemically reactive flow

Acceleration of turbulent combustion DNS via principal component transport

Derived scalar statistics from multiscalar measurements via surrogate composition spaces

Physics-Informed Neural Networks for Turbulent Combustion: Toward Extracting More Statistics and Closure from Point Multiscalar Measurements

Principal component analysis based combustion model in the context of a lifted methane/air flame: Sensitivity to the manifold parameters and subgrid closure

Experiment-Based Modeling of Turbulent Flames with Inhomogeneous Inlets

Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion

An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure

A numerical study of backdraft phenomena under normal and reduced gravity

A One-Dimensional Turbulence-Based Closure Model for Combustion LES

In the rain with and without an umbrella? The Reynolds transport theorem to the rescue

Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames

Collaborators (1)

M. Rafi Malik

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