31 citations · 41 across the 7 of their papers we have counts for
9 papers
NeRF-Inspired Neural Fields for 3D Super-resolution of Reacting-Flows
Dakshaka Vishwajith Amarathunga, Shang-Ru Li, Tarek Echekki
The multiscale nature of turbulent reacting flows and the importance of fine-scale structures pose challenges for super-resolution. We investigate a 3D super-resolution framework i…
A Neural Implicit Flow Super-Resolution Approach for Reacting Flows
Dakshaka Vishwajith Amarathunga, Lanre M. Oshinowo, Ji-Woong Park +1
We present a novel framework for reconstructing fine-resolution scalar fields from coarse-resolution data in 2D unsteady flames using an extension of neural implicit flow (NIF). Th…
Probabilistic transfer learning methodology to expedite high fidelity simulation of reactive flows
Bruno S. Soriano, Ki Sung Jung, Tarek Echekki +2
Reduced order models based on the transport of a lower dimensional manifold representation of the thermochemical state, such as Principal Component (PC) transport and Machine Learn…
Transfer learning for predicting source terms of principal component transport in chemically reactive flow
Ki Sung Jung, Tarek Echekki, Jacqueline H. Chen +1
The objective of this study is to evaluate whether the number of requisite training samples can be reduced with the use of various transfer learning models for predicting, for exam…
A Framework for Combustion Chemistry Acceleration with DeepONets
Anuj Kumar, Tarek Echekki
A combustion chemistry acceleration scheme is developed based on deep operator networks (DeepONets). The scheme is based on the identification of combustion reaction dynamics throu…
Generalized Joint Probability Density Function Formulation inTurbulent Combustion using DeepONet
Rishikesh Ranade, Kevin Gitushi, Tarek Echekki
Joint probability density function (PDF)-based models in turbulent combustion provide direct closure for turbulence-chemistry interactions. The joint PDFs capture the turbulent fla…
An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure
Rishikesh Ranade, Genong Li, Shaoping Li +1
Probability density function (PDF) based turbulent combustion modelling is limited by the need to store multi-dimensional PDF tables that can take up large amounts of memory. A sig…
A Framework for Data-Based Turbulent Combustion Closure: A Posteriori Validation
Rishikesh Ranade, Tarek Echekki
In this work, we demonstrate a framework for developing closure models in turbulent combustion using experimental multi-scalar measurements. The framework is based on the construct…
A Framework for Data-Based Turbulent Combustion Closure: A Priori Validation
Rishikesh Ranade, Tarek Echekki
Experimental multi-scalar measurements in laboratory flames have provided important databases for the validation of turbulent combustion closure models. In this work, we present a…