Rishikesh Ranade

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

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 429.

Deep Learning of Joint Scalar PDFs in Turbulent Flames from Sparse Multiscalar Data

Ranade, R., Gitushi, K. M., & Echekki, T. (2023, November 25). COMBUSTION SCIENCE AND TECHNOLOGY, Vol. 11.

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

Taassob, A., Ranade, R., & Echekki, T. (2023, October 31). ENERGY & FUELS, Vol. 10.

Algorithmically-consistent deep learning frameworks for structural topology optimization

Engineering Applications of Artificial Intelligence.

DiscretizationNet: A machine-learning based solver for Navier–Stokes equations using finite volume discretization

Computer Methods in Applied Mechanics and Engineering, 378, 113722.

Experiment-Based Modeling of Turbulent Flames with Inhomogeneous Inlets

Ranade, R., Echekki, T., & Masri, A. R. (2021, November 16). FLOW TURBULENCE AND COMBUSTION, Vol. 11.

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

COMBUSTION AND FLAME, 236.

DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization

(2020, May 17).

A Framework for Data-Based Turbulent Combustion Closure: A Priori Validation

Ranade, R., & Echekki, T. (2019, May 20). Combustion and Flame, Vol. 206, pp. 490–505.

A framework for data-based turbulent combustion closure: A posteriori validation

Combustion and Flame, 210, 279–291.

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

Combustion Science and Technology, 193(7), 1258–1277.

An extended hybrid chemistry framework for complex hydrocarbon fuels

FUEL, 251, 276–284.

An ANN based hybrid chemistry framework for complex fuels

FUEL, 241, 625–636.