| Identification of combustion mode under MILD conditions using chemical explosive mode analysis NAK Doan, S Bansude, K Osawa, Y Minamoto, T Lu, JH Chen, ... Proceedings of the Combustion Institute 38 (4), 5415-5422, 2021 | 16 | 2021 |
| A Data-Driven Framework for Computationally Efficient Integration of Chemical Kinetics Using Neural Ordinary Differential Equations S Bansude, F Imani, R Sheikhi ASME Open Journal of Engineering 2, 021022, 2023 | 4 | 2023 |
| Performance Assessment of Chemical Kinetics Neural Ordinary Differential Equations in Pairwise Mixing Stirred Reactor S Bansude, F Imani, R Sheikhi ASME Open Journal of Engineering 2, 021008, 2023 | 3 | 2023 |
| Direct Numerical Simulation of Multi-Injection Ignition in Low-Temperature Compression Ignition Environments M Rieth, M Day, S Bansude, T Lu, CB Kweon, J Temme, J Chen APS Division of Fluid Dynamics Meeting Abstracts, C05. 006, 2019 | 3 | 2019 |
| Implementation and Assessment of Chemical Kinetics Neural ODEs for Combustion Simulations S Bansude, F Imani, R Sheikhi APS Division of Fluid Dynamics Meeting Abstracts, J25.00001, 2022 | 1 | 2022 |
| Investigation of deep learning-based filtered density function for large eddy simulation of turbulent scalar mixing S Bansude, R Sheikhi Physics of Fluids 36 (1), 2024 | | 2024 |
| Deep Learning Models for Large Eddy Simulation of Turbulent Combustion S Bansude https://ctda-dev1-lib.grove.ad.uconn.edu/node/3721905, 2023 | | 2023 |
