| Actively learning gaussian process dynamics M Buisson-Fenet, F Solowjow, S Trimpe Learning for dynamics and control, 5-15, 2020 | 57 | 2020 |
| Control of piston position in inviscid gas by bilateral boundary actuation M Buisson-Fenet, S Koga, M Krstic 2018 IEEE Conference on Decision and Control (CDC), 5622-5627, 2018 | 14 | 2018 |
| Joint state and dynamics estimation with high-gain observers and Gaussian process models M Buisson-Fenet, V Morgenthaler, S Trimpe, F Di Meglio 2021 American Control Conference (ACC), 4027-4032, 2021 | 13 | 2021 |
| Towards gain tuning for numerical kkl observers M Buisson-Fenet, L Bahr, V Morgenthaler, F Di Meglio arXiv preprint arXiv:2204.00318, 2022 | 8 | 2022 |
| Learning to observe: neural network-based KKL observers M Buisson-Fenet, L Bahr, FD Meglio Python toolbox available at https://github. com/Centre-automatique-et …, 2022 | 5 | 2022 |
| Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs M Buisson-Fenet, V Morgenthaler, S Trimpe, F Di Meglio Transactions on Machine Learning Research, 2022 | 3 | 2022 |
| Learning dynamics from partial observations with structured neural ODEs M Buisson-Fenet, V Morgenthaler, S Trimpe, F Di Meglio arXiv preprint arXiv:2205.12550, 2022 | 2 | 2022 |
| Data-Driven Observability Analysis for Nonlinear Stochastic Systems PF Massiani, M Buisson-Fenet, F Solowjow, F Di Meglio, S Trimpe arXiv preprint arXiv:2302.11979, 2023 | 1 | 2023 |
| Experimental data assimilation: learning-based estimation for state-space models M Buisson-Fenet Université Paris sciences et lettres, 2023 | | 2023 |
| Actively Learning Dynamical Systems with Gaussian Processes M Buisson-Fenet, F Solowjow, S Trimpe Mines ParisTech, 2019 | | 2019 |
