| Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator Q Li, F Dietrich, EM Bollt, IG Kevrekidis Chaos: An Interdisciplinary Journal of Nonlinear Science 27 (10), 103111, 2017 | 292 | 2017 |
| On learning Hamiltonian systems from data T Bertalan, F Dietrich, I Mezić, IG Kevrekidis Chaos: An Interdisciplinary Journal of Nonlinear Science 29 (12), 121107, 2019 | 97 | 2019 |
| Gradient navigation model for pedestrian dynamics F Dietrich, G Köster Physical Review E 89 (6), 062801, 2014 | 80 | 2014 |
| Inter-Golgi transport mediated by COPI-containing vesicles carrying small cargoes PA Pellett, F Dietrich, J Bewersdorf, JE Rothman, G Lavieu Elife 2, 2013 | 55 | 2013 |
| The effect of stepping on pedestrian trajectories MJ Seitz, F Dietrich, G Köster Physica A: Statistical Mechanics and its Applications 421, 594-604, 2015 | 39 | 2015 |
| Bridging the gap: From cellular automata to differential equation models for pedestrian dynamics F Dietrich, G Köster, M Seitz, I von Sivers Journal of Computational Science 5 (5), 841-846, 2014 | 38 | 2014 |
| On matching, and even rectifying, dynamical systems through koopman operator eigenfunctions EM Bollt, Q Li, F Dietrich, I Kevrekidis SIAM Journal on Applied Dynamical Systems 17 (2), 1925-1960, 2018 | 29 | 2018 |
| Linking Gaussian process regression with data-driven manifold embeddings for nonlinear data fusion S Lee, F Dietrich, GE Karniadakis, IG Kevrekidis Interface focus 9 (3), 20180083, 2019 | 26 | 2019 |
| On the Koopman Operator of Algorithms F Dietrich, TN Thiem, IG Kevrekidis SIAM Journal on Applied Dynamical Systems 19 (2), 860-885, 2020 | 25 | 2020 |
| An emergent space for distributed data with hidden internal order through manifold learning FP Kemeth, SW Haugland, F Dietrich, T Bertalan, K Höhlein, Q Li, ... IEEE Access 6, 77402-77413, 2018 | 24 | 2018 |
| A study of pedestrian stepping behaviour for crowd simulation MJ Seitz, F Dietrich, G Köster Transportation Research Procedia 2, 282-290, 2014 | 24 | 2014 |
| Using Raspberry Pi for scientific video observation of pedestrians during a music festival DH Biedermann, F Dietrich, O Handel, PM Kielar, M Seitz http://dx.doi.org/10.13140/RG.2.1.4035.4407, 2015 | 23 | 2015 |
| Is Slowing Down Enough to Model Movement on Stairs? G Köster, D Lehmberg, F Dietrich Traffic and Granular Flow'15, 35-42, 2016 | 14 | 2016 |
| Local conformal autoencoder for standardized data coordinates E Peterfreund, O Lindenbaum, F Dietrich, T Bertalan, M Gavish, ... Proceedings of the National Academy of Sciences 117 (49), 30918-30927, 2020 | 13 | 2020 |
| Some manifold learning considerations toward explicit model predictive control RJ Lovelett, F Dietrich, S Lee, IG Kevrekidis AIChE Journal 66 (5), e16881, 2020 | 12 | 2020 |
| Learning emergent PDEs in a learned emergent space FP Kemeth, T Bertalan, T Thiem, F Dietrich, SJ Moon, CR Laing, ... arXiv preprint arXiv:2012.12738, 2020 | 11 | 2020 |
| The Superposition Principle: A Conceptual Perspective on Pedestrian Stream Simulations MJ Seitz, F Dietrich, G Köster, HJ Bungartz Collective Dynamics 1, 1-19, 2016 | 11 | 2016 |
| Learning effective stochastic differential equations from microscopic simulations: combining stochastic numerics and deep learning F Dietrich, A Makeev, G Kevrekidis, N Evangelou, T Bertalan, S Reich, ... arXiv preprint arXiv:2106.09004, 2021 | 10 | 2021 |
| Optimal Transport on the Manifold of SPD Matrices for Domain Adaptation O Yair, F Dietrich, R Talmon, IG Kevrekidis arXiv preprint arXiv:1906.00616, 2019 | 10 | 2019 |
| FAST AND FLEXIBLE UNCERTAINTY QUANTIFICATION THROUGH A DATA-DRIVEN SURROGATE MODEL F Dietrich, F Künzner, T Neckel, G Köster, HJ Bungartz International Journal for Uncertainty Quantification 8 (2), 2018 | 10 | 2018 |
Ioannis KevrekidisJohns Hopkins UniversityBestätigte E-Mail-Adresse bei jhu.edu
