Collaborating institutions: Aurora Flight Sciences, Wisk
Relevant papers
- Luca Scotzniovsky, John T. Hwang. VortexAD: A Graph-Based Linear Potential Flow Analysis Framework for Large-Scale MDO. AIAA AVIATION 2026 Forum, AIAA 2026-4642, 2026.PDF
@inproceedings{scotzniovsky2026vortexad, author = {Luca Scotzniovsky and John T. Hwang}, title = {VortexAD: A Graph-Based Linear Potential Flow Analysis Framework for Large-Scale MDO}, booktitle = {AIAA AVIATION 2026 Forum, AIAA 2026-4642}, year = {2026}, note = {Metadata verification: Crossref unavailable: HTTPError} } - Anugrah Jo Joshy, Jonathan Zerez, John T. Hwang. Enhancing Robustness and Efficiency in Large-Scale System Design Optimization Using Hessian-Vector Products. ASME IDETC/CIE 2026, Houston, Aug 23–26; public domain at conference, 2026. Manuscript
@inproceedings{joshy2026enhancing, author = {Anugrah Jo Joshy and Jonathan Zerez and John T. Hwang}, title = {Enhancing Robustness and Efficiency in Large-Scale System Design Optimization Using Hessian-Vector Products}, booktitle = {ASME IDETC/CIE 2026, Houston, Aug 23–26; public domain at conference}, year = {2026} } - Anugrah Jo Joshy, John T. Hwang. modOpt: A modular development environment and library for optimization algorithms. Advances in Engineering Software, 2026.DOIPDF
@article{joshy2026modopt, author = {Anugrah Jo Joshy and John T. Hwang}, title = {modOpt: A modular development environment and library for optimization algorithms}, journal = {Advances in Engineering Software}, year = {2026}, volume = {213}, pages = {104084}, doi = {10.1016/j.advengsoft.2025.104084}, url = {https://doi.org/10.1016/j.advengsoft.2025.104084}, eprint = {2410.12942}, archiveprefix = {arXiv}, note = {Metadata verification: Crossref DOI} } - Marius L. Ruh, John T. Hwang. Surface Mesh Deformation for Large-Scale Multidisciplinary Design Optimization of Aircraft Concepts. AIAA AVIATION 2026 Forum, AIAA 2026-4569, 2026.PDF
@inproceedings{ruh2026surface, author = {Marius L. Ruh and John T. Hwang}, title = {Surface Mesh Deformation for Large-Scale Multidisciplinary Design Optimization of Aircraft Concepts}, booktitle = {AIAA AVIATION 2026 Forum, AIAA 2026-4569}, year = {2026}, note = {Metadata verification: Crossref unavailable: HTTPError} } - Luca Scotzniovsky, John T. Hwang. A Fast, Memory-Efficient Panel Method for Large-Scale Multidisciplinary Design Optimization Under Uncertainty Using Graph-Based Modeling. AIAA AVIATION 2025 Forum, 2025.PDF
@inproceedings{scotzniovsky2025fast, author = {Luca Scotzniovsky and John T. Hwang}, title = {A Fast, Memory-Efficient Panel Method for Large-Scale Multidisciplinary Design Optimization Under Uncertainty Using Graph-Based Modeling}, booktitle = {AIAA AVIATION 2025 Forum}, year = {2025} } - Bingran Wang, Marius L. Ruh, Aoran Tian, Luca Scotzniovsky, John T. Hwang. Large-scale MDO under uncertainty of an eVTOL aircraft using dimension reduction via global sensitivity analysis. AIAA AVIATION FORUM AND ASCEND 2025, 2025.DOIPDF
@inproceedings{wang2025large, author = {Bingran Wang and Marius L. Ruh and Aoran Tian and Luca Scotzniovsky and John T. Hwang}, title = {Large-scale MDO under uncertainty of an eVTOL aircraft using dimension reduction via global sensitivity analysis}, booktitle = {AIAA AVIATION FORUM AND ASCEND 2025}, year = {2025}, doi = {10.2514/6.2025-3344}, url = {https://doi.org/10.2514/6.2025-3344}, note = {Metadata verification: Crossref title} } - Anugrah Jo Joshy, John T. Hwang. PySLSQP: A transparent Python package for the SLSQP optimization algorithm modernized with utilities for visualization and post-processing. Journal of Open Source Software, 2024.DOI
@article{joshy2024pyslsqp, title = {PySLSQP: A transparent Python package for the SLSQP optimization algorithm modernized with utilities for visualization and post-processing}, author = {Anugrah Jo Joshy and John T. Hwang}, journal = {Journal of Open Source Software}, year = {2024}, volume = {9}, number = {103}, pages = {7246}, doi = {10.21105/joss.07246} }
This project develops multidisciplinary design optimization algorithms that can efficiently optimize hundreds of design parameters for hybrid- and turbo-electric regional aircraft. It combines aerodynamic and powertrain models into scalable optimization methods.
Research connections