| Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain D Lucente, J Rolland, C Herbert, F Bouchet Journal of Statistical Mechanics: Theory and Experiment 2022 (8), 083201, 2022 | 18 | 2022 |
| Machine learning of committor functions for predicting high impact climate events D Lucente, S Duffner, C Herbert, J Rolland, F Bouchet arXiv preprint arXiv:1910.11736, 2019 | 17 | 2019 |
| Committor functions for climate phenomena at the predictability margin: The example of El Niño–Southern Oscillation in the Jin and Timmermann model D Lucente, C Herbert, F Bouchet Journal of the Atmospheric Sciences 79 (9), 2387-2400, 2022 | 15 | 2022 |
| Inference of time irreversibility from incomplete information: Linear systems and its pitfalls D Lucente, A Baldassarri, A Puglisi, A Vulpiani, M Viale Physical Review Research 4 (4), 043103, 2022 | 13 | 2022 |
| Revealing the nonequilibrium nature of a granular intruder: the crucial role of non-Gaussian behavior D Lucente, M Viale, A Gnoli, A Puglisi, A Vulpiani Physical Review Letters 131 (7), 078201, 2023 | 5 | 2023 |
| Out-of-Equilibrium Non-Gaussian Behavior in Driven Granular Gases D Lucente, M Viale, A Gnoli, A Puglisi, A Vulpiani arXiv e-prints, arXiv: 2302.06937, 2023 | 2 | 2023 |
| Predicting probabilities of climate extremes from observations and dynamics D Lucente Université de Lyon, 2021 | 2 | 2021 |
| Inference in non-equilibrium systems from incomplete information: the case of linear systems and its pitfalls D Lucente, A Baldassarri, A Puglisi, A Vulpiani, M Viale arXiv preprint arXiv:2205.08961, 2022 | 1 | 2022 |
| A Markovian approach to the Prandtl–Tomlinson frictional model D Lucente, A Petri, A Vulpiani Physica A: Statistical Mechanics and its Applications 572, 125899, 2021 | 1 | 2021 |
| Granular systems: the complexity of non-equilibrium A Puglisi, L de Arcangelis, A Plati, A Gnoli, A Sarracino, E Lippiello, ... Bulletin of the American Physical Society, 2024 | | 2024 |
| Extreme heat wave sampling and prediction with analog Markov chain and comparisons with deep learning G Miloshevich, D Lucente, P Yiou, F Bouchet Environmental Data Science 3, e9, 2024 | | 2024 |
| Random exchange dynamics with bounds: H-theorem and negative temperature D Lucente, M Baldovin, A Puglisi, A Vulpiani arXiv preprint arXiv:2312.12017, 2023 | | 2023 |
| Statistical features of systems driven by non-Gaussian processes: theory & practice D Lucente, A Puglisi, M Viale, A Vulpiani Journal of Statistical Mechanics: Theory and Experiment 2023 (11), 113202, 2023 | | 2023 |
| Stochastic weather generator and deep learning approach for predicting and sampling extreme European heatwaves G Miloshevich, D Lucente, F Bouchet, P Yiou EGU General Assembly Conference Abstracts, EGU-6131, 2023 | | 2023 |
| Chasing data-driven probabilistic forecasting tools for heatwaves: the case of analog Markov chains and dimensional reduction via autoencoders G Miloshevich, F Bouchet, D Lucente, P Yiou AGU Fall Meeting Abstracts 2022, NG42B-0406, 2022 | | 2022 |
| Advances in rare event simulations using data-based estimation of committor functions D Lucente, J Rolland, C Herbert, F Bouchet EGU General Assembly Conference Abstracts, EGU22-4021, 2022 | | 2022 |
| Quasi-stationary Rossby waves, teleconnection patterns and extreme heat waves studied with a rare event algorithm F Bouchet, F Ragone, D Lucente, G Miloshevich AGU Fall Meeting 2021, 2021 | | 2021 |
| Predicting extreme events using dynamics based machine learning. D Lucente, G Miloshevich, C Herbert, F Bouchet EGU General Assembly Conference Abstracts, EGU21-14436, 2021 | | 2021 |
| Drivers of midlatitude extreme heat waves revealed by analogues and machine learning G Miloshevich, D Lucente, C Herbert, F Bouchet EGU General Assembly Conference Abstracts, EGU21-15642, 2021 | | 2021 |
| New ways for dynamical prediction of extreme heat waves: rare event simulations and stochastic process-based machine learning. F Bouchet, F Ragone, D Lucente, G Miloshevich, C Herbert APS Division of Fluid Dynamics Meeting Abstracts, H02. 005, 2021 | | 2021 |
