Published in AIAA Scitech, 2023
Frameworks and analysis on multi-fidelity linear regression methods in data-poor regimes.
Recommended citation: Sella, V., Pham, J., Chaudhuri, A., Willcox, K. (2023). "Multi-fidelity deep learning for regression in data-poor applications." AIAA Scitech 2023.
Published in Preprint, 2022
Accurate, inexpensive surrogates for inverse design maps – the map from performance requirements to optimal designs.
Recommended citation: Chaudhuri, A., Sella, V., O'Leary-Roseberry, T., Du, X., Guo, M., Ghattas, O., Martins, J., Willcox, K. (2022). "Projection Enabled Multifidelity Neural Networks for Tradespace Exploration." Preprint.
Published in International Conference on High Performance Computing, 2021
Convolutional neural network (CNN) techniques for aerodynamic performance evaluation.
Recommended citation: Luo, S., Cui, J., Sella, V., Liu, J., Koric, S., Kindratenko, V. (2021). Turbomachinery Blade Surrogate Modeling Using Deep Learning.ISC High Performance 2021 Springer, Cham. https://doi.org/10.1007/978-3-030-90539-2_6
Published in AIAA Propulsion & Energy, 2020
Design of a hybrid rocket engine designed using novel nitrous oxide-oxygen mixture (Nytrox) and paraffin.
Recommended citation: Sella, V., Larkey, A., Lembeck, M., et al. (2020). "Development of a Nytrox-Paraffin Hybrid Rocket Engine." AIAA P&E 2020. https://arc.aiaa.org/doi/10.2514/6.2020-3729