| On learning Hamiltonian systems from data T Bertalan, F Dietrich, I Mezić, IG Kevrekidis Chaos: An Interdisciplinary Journal of Nonlinear Science 29 (12), 2019 | 143 | 2019 |
| Alpha-particle radiotherapy: For large solid tumors diffusion trumps targeting C Zhu, M Sempkowski, T Holleran, T Linz, T Bertalan, A Josefsson, ... Biomaterials 130, 67-75, 2017 | 42 | 2017 |
| 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 | 26 | 2018 |
| Emergent spaces for coupled oscillators TN Thiem, M Kooshkbaghi, T Bertalan, CR Laing, IG Kevrekidis Frontiers in computational neuroscience 14, 36, 2020 | 25 | 2020 |
| 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 | 23 | 2020 |
| Coarse-Grained Descriptions of Dynamics for Networks with both Intrinsic and Structural Heterogeneities IGK Tom Bertalan, Yan Wu, Carlo Laing, C William Gear Frontiers in Computational Neuroscience, 2017 | 12 | 2017 |
| Dimension reduction in heterogeneous neural networks: generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA) M Choi, T Bertalan, CR Laing, IG Kevrekidis The European Physical Journal Special Topics 225, 1165-1180, 2016 | 11 | 2016 |
| nSpyres, An OpenSource, Python Based Framework for Simulation of Flow through Porous Media ES Carlson, AW Islam, F Dumkwu, TS Bertalan 4th International Conference on Porous Media and Annual Meeting of the …, 2012 | 10 | 2012 |
| OpenMG: a new multigrid implementation in Python TS Bertalan, AW Islam, RB Sidje, ES Carlson Numerical Linear Algebra with Applications 21 (5), 685-700, 2014 | 6 | 2014 |
| An equal space for complex data with unknown internal order: Observability, gauge invariance and manifold learning FP Kemeth, SW Haugland, F Dietrich, T Bertalan, Q Li, EM Bollt, R Talmon, ... arXiv preprint arXiv:1708.05406, 2017 | 2 | 2017 |
| Certified Invertibility in Neural Networks via Mixed-Integer Programming T Cui, T Bertalan, GJ Pappas, M Morari, Y Kevrekidis, M Fazlyab Learning for Dynamics and Control Conference, 483-496, 2023 | 1 | 2023 |
| Learning Partial Differential Equations in Emergent Coordinates F Kemeth, T Bertalan, T Thiem, IG Kevrekidis 2021 AIChE Annual Meeting, 2021 | 1 | 2021 |
| Tipping Point Dynamics for Epidemiological Networks. Constructing Reduced Dynamical Data-Driven Models for Evolving Graphs N Evangelou, T Cui, J Bello-Rivas, A Makeev, T Bertalan, IG Kevrekidis 2022 AIChE Annual Meeting, 2022 | | 2022 |
| A Data-Driven Approach to Determining Problem Well-Posedness T Bertalan, E Rebrova, G Kevrekidis, IG Kevrekidis 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Some of the Variables, Some of the Times, with Some Things Known: Identification with Partial Information S Malani, T Bertalan, T Cui, M Betenbaugh, J Avalos, IG Kevrekidis 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Manifold Learning Post-Processing Galerkin Algorithms for Dissipative PDEs on Their Approximate Inertial Manifolds CM Linares, T Bertalan, N Evangelou, ES Titi, IG Kevrekidis 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Quantifying the Invertibility of Neural Networks and Their Transformations T Cui, T Bertalan, IG Kevrekidis, M Fazlyab 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Inverse Backward Analysis of Neural Approximants of Ordinary Differential Equations T Bertalan, A Zhu, B Zhu, Y Tang, IG Kevrekidis 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Addressing Well-Posedness in a Data-Driven Manner: Model Problems and Physics-Informed Neural Networks T Bertalan, G Kevrekidis, S Mishra, IG Kevrekidis 2021 AIChE Annual Meeting, 2021 | | 2021 |
| Data-Driven Development of Approximate Inertial Forms and Closures for Coarse-Scale Modeling of Multiphase Flows CM Linares, T Bertalan, S Lee, J Lu, G Tryggvason, IG Kevrekidis 2021 AIChE Annual Meeting, 2021 | | 2021 |
Felix DietrichProfessor for Physics-Enhanced Machine Learning, Technical University of Munich在 tum.de 的电子邮件经过验证
