| NOMAD: A distributed web-based platform for managing materials science research data M Scheidgen, L Himanen, AN Ladines, D Sikter, M Nakhaee, Á Fekete, ... Journal of Open Source Software 8 (90), 5388, 2023 | 26 | 2023 |
| Walking on stairs: Experiment and model G Köster, D Lehmberg, A Kneidl Physical Review E 100 (2), 022310, 2019 | 23 | 2019 |
| Double diffusion maps and their latent harmonics for scientific computations in latent space N Evangelou, F Dietrich, E Chiavazzo, D Lehmberg, M Meila, ... Journal of Computational Physics 485, 112072, 2023 | 17 | 2023 |
| Is slowing down enough to model movement on stairs? G Köster, D Lehmberg, F Dietrich Traffic and Granular Flow'15, 35-42, 2016 | 15 | 2016 |
| datafold: data-driven models for point clouds and time series on manifolds D Lehmberg, F Dietrich, G Köster, HJ Bungartz Journal of Open Source Software 5 (51), 2283, 2020 | 14 | 2020 |
| Modeling Melburnians—Using the Koopman operator to gain insight into crowd dynamics D Lehmberg, F Dietrich, G Köster Transportation Research Part C: Emerging Technologies 133, 103437, 2021 | 10 | 2021 |
| Traffic and Granular Flow’15 G Köster, D Lehmberg, F Dietrich Springer, 2016 | 6 | 2016 |
| Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator IK Gallos, D Lehmberg, F Dietrich, C Siettos Chaos: An Interdisciplinary Journal of Nonlinear Science 34 (1), 2024 | 5 | 2024 |
| Can we learn where people go? M Gödel, G Köster, D Lehmberg, M Gruber, A Kneidl, F Sesser arXiv preprint arXiv:1812.03719, 2018 | 5 | 2018 |
| Exploring Koopman operator based surrogate models—accelerating the analysis of critical pedestrian densities D Lehmberg, F Dietrich, IG Kevrekidis, HJ Bungartz, G Köster Traffic and Granular Flow 2019, 149-157, 2020 | 3 | 2020 |
| Toward learning dynamic origin-destination matrices from crowd density heatmaps M Gödel, D Lehmberg, R Brydon, E Bosina, G Köster Journal of Statistical Mechanics: Theory and Experiment 2022 (5), 053401, 2022 | 1 | 2022 |
| Operator-informed machine learning: Extracting geometry and dynamics from time series data D Lehmberg Technische Universität München, 2022 | | 2022 |
| Scientific Computation in the Latent Space through Manifold Learning N Evangelou, F Dietrich, D Lehmberg, G Psarellis, E Chiavazzo, ... 2020 Virtual AIChE Annual Meeting, 2020 | | 2020 |
| Estimating Potential Power Supply of an Offshore Wind Farm using Machine Learning D Lehmberg | | 2018 |
