| 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 | 20 | 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, 2023 | 5 | 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, 2023 | 4 | 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 Bulletin of the American Physical Society 67, 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 University of Connecticut (https://ctda-dev1-lib.grove.ad.uconn.edu/node …, 2023 | | 2023 |
