| Projection-based model reduction: Formulations for physics-based machine learning R Swischuk, L Mainini, B Peherstorfer, K Willcox Computers & Fluids 179, 704-717, 2019 | 380 | 2019 |
| Surrogate Modeling Approach to Support Real-time Structural Assessment and Decision-making L Mainini, K Willcox AIAA Journal 53 (6), 1612-1626, 2015 | 88 | 2015 |
| A surrogate modeling approach to support real-time structural assessment and decision-making L Mainini, K Willcox 10th AIAA Multidisciplinary Design Optimization Conference, National Harbor …, 2014 | 88 | 2014 |
| Multifidelity DDDAS methods with application to a self-aware aerospace vehicle D Allaire, D Kordonowy, M Lecerf, L Mainini, K Willcox Procedia Computer Science 29, 1182-1192, 2014 | 61 | 2014 |
| Real-time fault detection and prognostics for aircraft actuation systems PCC Berri, MDL Dalla Vedova, L Mainini AIAA Scitech 2019 Forum, 2210, 2019 | 34 | 2019 |
| Computational framework for real-time diagnostics and prognostics of aircraft actuation systems PC Berri, MDL Dalla Vedova, L Mainini Computers in Industry 132, 103523, 2021 | 32 | 2021 |
| Data to decisions: Real-time structural assessment from sparse measurements affected by uncertainty L Mainini, KE Willcox Computers & Structures 182, 296-312, 2017 | 32 | 2017 |
| An offline/online DDDAS capability for self-aware aerospace vehicles D Allaire, J Chambers, R Cowlagi, D Kordonowy, M Lecerf, L Mainini, ... Procedia Computer Science 18, 1959-1968, 2013 | 32 | 2013 |
| Multidisciplinary design optimization for hybrid electric vehicles: component sizing and multi-fidelity frontal crashworthiness PG Anselma, CB Niutta, L Mainini, G Belingardi Structural and Multidisciplinary Optimization 62, 2149-2166, 2020 | 28 | 2020 |
| Analytical benchmark problems for multifidelity optimization methods L Mainini, A Serani, MP Rumpfkeil, E Minisci, D Quagliarella, H Pehlivan, ... arXiv preprint arXiv:2204.07867, 2022 | 27 | 2022 |
| Active learning and bayesian optimization: a unified perspective to learn with a goal F Di Fiore, M Nardelli, L Mainini Archives of Computational Methods in Engineering, 1-29, 2024 | 26 | 2024 |
| A modelling framework to support power architecture trade-off studies for More-Electric Aircraft AG Garriga, P Govindaraju, SS Ponnusamy, N Cimmino, L Mainini Transportation Research Procedia 29, 146-156, 2018 | 24 | 2018 |
| Learning for predictions: Real-time reliability assessment of aerospace systems PC Berri, MDL Dalla Vedova, L Mainini AIAA Journal 60 (2), 566-577, 2022 | 22 | 2022 |
| A machine learning enabled multi-fidelity platform for the integrated design of aircraft systems AG Garriga, L Mainini, SS Ponnusamy Journal of Mechanical Design 141 (12), 121405, 2019 | 19 | 2019 |
| Multifidelity domain-aware learning for the design of re-entry vehicles F Di Fiore, P Maggiore, L Mainini Structural and Multidisciplinary Optimization 64, 3017-3035, 2021 | 18 | 2021 |
| A multi-fidelity framework to support the design of More-Electric Actuation A Garcia Garriga, SS Ponnusamy, L Mainini 2018 multidisciplinary analysis and optimization conference, 3741, 2018 | 18 | 2018 |
| Sensor placement strategy to inform decisions L Mainini, KE Willcox 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 3820, 2017 | 14 | 2017 |
| Multidisciplinary integrated framework for the optimal design of a jet aircraft wing L Mainini, P Maggiore International Journal of Aerospace Engineering 2012 (1), 750642, 2012 | 13 | 2012 |
| Resource Aware Multifidelity Active Learning for Efficient Optimization F Grassi, G Manganini, M Garraffa, L Mainini 2021 AIAA SciTech Forum, 2021 | 11 | 2021 |
| Resource Aware Multifidelity Active Learning for Efficient Optimization F Grassi, G Manganini, M Garraffa, L Mainini arXiv preprint arXiv:2007.04674, 2020 | 11 | 2020 |
Karen WillcoxOden Institute for Computational Engineering and Sciences, UT AustinEmail verificata su oden.utexas.edu
