| Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning F Dietrich, A Makeev, G Kevrekidis, N Evangelou, T Bertalan, S Reich, ... Chaos: An Interdisciplinary Journal of Nonlinear Science 33 (2), 2023 | 40 | 2023 |
| 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 |
| Machine learning-assisted crystal engineering of a zeolite X Li, H Han, N Evangelou, NJ Wichrowski, P Lu, W Xu, SJ Hwang, ... Nature communications 14 (1), 3152, 2023 | 12 | 2023 |
| On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non) identifiability N Evangelou, NJ Wichrowski, GA Kevrekidis, F Dietrich, M Kooshkbaghi, ... PNAS nexus 1 (4), pgac154, 2022 | 9 | 2022 |
| From partial data to out-of-sample parameter and observation estimation with diffusion maps and geometric harmonics ED Koronaki, N Evangelou, YM Psarellis, AG Boudouvis, IG Kevrekidis Computers & Chemical Engineering 178, 108357, 2023 | 7 | 2023 |
| Initializing LSTM internal states via manifold learning FP Kemeth, T Bertalan, N Evangelou, T Cui, S Malani, IG Kevrekidis Chaos: An Interdisciplinary Journal of Nonlinear Science 31 (9), 2021 | 7 | 2021 |
| Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal Particles N Evangelou, F Dietrich, JM Bello-Rivas, A Yeh, R Stein, MA Bevan, ... arXiv preprint arXiv:2205.00286, 2022 | 6 | 2022 |
| Tasks makyth models: Machine learning assisted surrogates for tipping points G Fabiani, N Evangelou, T Cui, JM Bello-Rivas, CP Martin-Linares, ... arXiv preprint arXiv:2309.14334, 2023 | 4 | 2023 |
| On the parameter combinations that matter and on those that do not N Evangelou, NJ Wichrowski, GA Kevrekidis, F Dietrich, M Kooshkbaghi, ... arXiv preprint arXiv:2110.06717, 2021 | 3 | 2021 |
| Task-oriented machine learning surrogates for tipping points of agent-based models G Fabiani, N Evangelou, T Cui, JM Bello-Rivas, CP Martin-Linares, ... Nature communications 15 (1), 4117, 2024 | 2 | 2024 |
| Tipping points of evolving epidemiological networks: Machine learning-assisted, data-driven effective modeling N Evangelou, T Cui, JM Bello-Rivas, A Makeev, IG Kevrekidis Chaos: An Interdisciplinary Journal of Nonlinear Science 34 (6), 2024 | | 2024 |
| Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era ED Koronaki, N Evangelou, CP Martin-Linares, ES Titi, IG Kevrekidis Journal of Computational Physics 506, 112910, 2024 | | 2024 |
| Active learning of ternary alloy structures and energies G Deshmukh, NJ Wichrowski, N Evangelou, PG Ghanekar, S Deshpande, ... npj Computational Materials 10 (1), 116, 2024 | | 2024 |
| From Noise to Signal: Unveiling Treatment Effects from Digital Health Data through Pharmacology-Informed Neural-SDE S Pakravan, N Evangelou, M Usdin, L Brooks, J Lu arXiv preprint arXiv:2403.03274, 2024 | | 2024 |
| CONSTRUCTING REDUCED DATA-DRIVEN MODELS FOR DYNAMICAL SYSTEMS USING MANIFOLD-AND DEEP LEARNING N Evangelou Johns Hopkins University, 2024 | | 2024 |
| Data-Driven Bifurcation Diagrams for Neural (Integro-) Differential Equations G Fabiani, N Evangelou, T Cui, DG Giovanis, IG Kevrekidis 2023 AIChE Annual Meeting, 2023 | | 2023 |
| Data-Driven Disentanglement of Latent variables and Effective parameters for Cellular Trajectories with Manifold Learning and Deep Learning Approaches N Evangelou, A Georgiou, D Sroczynski, J Phillip, IG Kevrekidis 2023 AIChE Annual Meeting, 2023 | | 2023 |
| Active Learning Workflow for Discovery of Stable Ternary Alloys from Binary Alloy Data G Deshmukh, NJ Wichrowski, N Evangelou, P Ghanekar, S Deshpande, ... 2023 AIChE Annual Meeting, 2023 | | 2023 |
| Machine Learning for the identification of phase-transitions in interacting agent-based systems N Evangelou, DG Giovanis, GA Kevrekidis, GA Pavliotis, IG Kevrekidis arXiv preprint arXiv:2310.19039, 2023 | | 2023 |
| Machine Learning-Based Coarse-Scale Identification and Bifurcation Analysis for Modelling Tipping Points: a Financial Market Agent-Based Illustration G Fabiani, N Evangelou, T Cui, JM Bello-Rivas, C Martinez-Linares, ... | | 2023 |
Felix DietrichProfessor for Physics-Enhanced Machine Learning, Technical University of Munich在 tum.de 的电子邮件经过验证
