| Inertia location and slow network modes determine disturbance propagation in large-scale power grids L Pagnier, P Jacquod PloS one 14 (3), e0213550, 2019 | 78 | 2019 |
| The key player problem in complex oscillator networks and electric power grids: Resistance centralities identify local vulnerabilities M Tyloo, L Pagnier, P Jacquod Science advances 5 (11), eaaw8359, 2019 | 77 | 2019 |
| Physics-informed graphical neural network for parameter & state estimations in power systems L Pagnier, M Chertkov arXiv preprint arXiv:2102.06349, 2021 | 50 | 2021 |
| Optimal Placement of Inertia and Primary Control: A Matrix Perturbation Theory Approach L Pagnier, P Jacquod IEEE Access 7, 145889-145900, 2019 | 42 | 2019 |
| Disturbance propagation, inertia location and slow modes in large-scale high voltage power grids L Pagnier, P Jacquod arXiv preprint arXiv:1810.04982, 2018 | 14 | 2018 |
| Toward model reduction for power system transients with physics-informed PDE L Pagnier, J Fritzsch, P Jacquod, M Chertkov IEEE Access 10, 65118-65125, 2022 | 13 | 2022 |
| How fast can one overcome the paradox of the energy transition? A physico-economic model for the European power grid L Pagnier, P Jacquod Energy 157, 550-560, 2018 | 12 | 2018 |
| Embedding power flow into machine learning for parameter and state estimation L Pagnier, M Chertkov arXiv preprint arXiv:2103.14251, 2021 | 11 | 2021 |
| Optimal placement of inertia and primary control in high voltage power grids P Jacquod, L Pagnier 2019 53rd Annual Conference on Information Sciences and Systems (CISS), 1-6, 2019 | 10 | 2019 |
| A predictive pan-European economic and production dispatch model for the energy transition in the electricity sector L Pagnier, P Jacquod 2017 IEEE Manchester PowerTech, 1-6, 2017 | 9 | 2017 |
| Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study R Ferrando, L Pagnier, R Mieth, Z Liang, Y Dvorkin, D Bienstock, ... IEEE Transactions on Energy Markets, Policy and Regulation, 2023 | 6 | 2023 |
| Locating line and node disturbances in networks of diffusively coupled dynamical agents R Delabays, L Pagnier, M Tyloo New Journal of Physics 23 (4), 043037, 2021 | 5 | 2021 |
| Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems. arXiv 2021 L Pagnier, M Chertkov arXiv preprint arXiv:2102.06349, 0 | 5 | |
| Locating fast-varying line disturbances with the frequency mismatch R Delabays, L Pagnier, M Tyloo IFAC-PapersOnLine 55 (13), 270-275, 2022 | 4 | 2022 |
| PanTaGruEl-a pan-European transmission grid and electricity generation model (Zenodo Rep.) L Pagnier, P Jacquod | 4 | 2019 |
| Large electric load fluctuations in energy-efficient buildings and how to suppress them with demand side management T Coletta, R Delabays, L Pagnier, P Jacquod 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT …, 2016 | 4 | 2016 |
| Control of line pack in natural gas system: Balancing limited resources under uncertainty C Hyett, L Pagnier, J Alisse, L Sabban, I Goldshtein, M Chertkov PSIG Annual Meeting, PSIG-2314, 2023 | 2 | 2023 |
| Physics-Informed Critic in an Actor-Critic Reinforcement Learning for Swimming in Turbulence C Koh, L Pagnier, M Chertkov arXiv preprint arXiv:2406.10242, 2024 | 1 | 2024 |
| Machine learning for electricity market clearing L Pagnier, R Ferrando, Y Dvorkin, M Chertkov arXiv preprint arXiv:2205.11641, 2022 | 1 | 2022 |
| Swissgrid’s strategic grid 2025: an independent analysis L Pagnier, P Jacquod | 1 | 2018 |
