Rishikesh Ranade

Ranade, R., Gitushi, K. M., & Echekki, T. (2023, November 25). Deep Learning of Joint Scalar PDFs in Turbulent Flames from Sparse Multiscalar Data. COMBUSTION SCIENCE AND TECHNOLOGY, Vol. 11. https://doi.org/10.1080/00102202.2023.2283816

Taassob, A., Ranade, R., & Echekki, T. (2023, October 31). Physics-Informed Neural Networks for Turbulent Combustion: Toward Extracting More Statistics and Closure from Point Multiscalar Measurements. ENERGY & FUELS, Vol. 10. https://doi.org/10.1021/acs.energyfuels.3c02410

Gitushi, K. M., Ranade, R., & Echekki, T. (2022). Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion. COMBUSTION AND FLAME, 236. https://doi.org/10.1016/j.combustflame.2021.111814

Rade, J., Balu, A., Herron, E., Pathak, J., Ranade, R., Sarkar, S., & Krishnamurthy, A. (2021). Algorithmically-consistent deep learning frameworks for structural topology optimization. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2021.104483

Ranade, R., Li, G., Li, S., & Echekki, T. (2021). An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure. Combustion Science and Technology, 193(7), 1258–1277. https://doi.org/10.1080/00102202.2019.1686702

Ranade, R., Hill, C., & Pathak, J. (2021). DiscretizationNet: A machine-learning based solver for Navier–Stokes equations using finite volume discretization. Computer Methods in Applied Mechanics and Engineering, 378, 113722. https://doi.org/10.1016/j.cma.2021.113722

Ranade, R., Echekki, T., & Masri, A. R. (2021, November 16). Experiment-Based Modeling of Turbulent Flames with Inhomogeneous Inlets. FLOW TURBULENCE AND COMBUSTION, Vol. 11. https://doi.org/10.1007/s10494-021-00304-8

DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization. (2020, May 17).

Ranade, R., & Echekki, T. (2019, May 20). A Framework for Data-Based Turbulent Combustion Closure: A Priori Validation. Combustion and Flame, Vol. 206, pp. 490–505. https://doi.org/10.1016/j.combustflame.2019.05.028

Ranade, R., & Echekki, T. (2019). A framework for data-based turbulent combustion closure: A posteriori validation. Combustion and Flame, 210, 279–291. https://doi.org/10.1016/j.combustflame.2019.08.039

Ranade, R., Alqahtani, S., Farooq, A., & Echekki, T. (2019). An ANN based hybrid chemistry framework for complex fuels. FUEL, 241, 625–636. https://doi.org/10.1016/j.fuel.2018.12.082

Ranade, R., Alqahtani, S., Farooq, A., & Echekki, T. (2019). An extended hybrid chemistry framework for complex hydrocarbon fuels. FUEL, 251, 276–284. https://doi.org/10.1016/j.fuel.2019.04.053