Initializing LSTM internal states via manifold learning. - Felix P. Kemeth; Tom Bertalan; Nikolaos Evangelou; Tianqi Cui; Saurabh Malani; Ioannis G. Kevrekidis (CoRR, 2021)

Learning effective stochastic differential equations from microscopic simulations: combining stochastic numerics and deep learning. - Felix Dietrich; Alexei Makeev; George Kevrekidis; Nikolaos Evangelou; Tom Bertalan; Sebastian Reich; Ioannis G. Kevrekidis (CoRR, 2021)

On the Parameter Combinations That Matter and on Those That do Not. - Nikolaos Evangelou; Noah J. Wichrowski; George A. Kevrekidis; Felix Dietrich; Mahdi Kooshkbaghi; Sarah McFann; Ioannis G. Kevrekidis (CoRR, 2021)

Bounding Box Based Weakly Supervised Deep Convolutional Neural Network for Medical Image Segmentation Using an Uncertainty Guided and Spatially Constrained Loss. - Golnar K. Mahani; Ruizhe Li 005; Nikolaos Evangelou; Stamatios Sotiropolous; Paul S. Morgan; Andrew P. French; Xin Chen 003 (ISBI, 2022)

Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal Particles. - Nikolaos Evangelou; Felix Dietrich; Juan M. Bello-Rivas; Alex Yeh; Rachel Stein; Michael A. Bevan; Ioannis G. Kevrekidis (CoRR, 2022)

Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points. - Gianluca Fabiani; Nikolaos Evangelou; Tianqi Cui; Juan M. Bello-Rivas; Cristina P. Martin-Linares; Constantinos I. Siettos; Ioannis G. Kevrekidis (CoRR, 2023)

Machine Learning for the identification of phase-transitions in interacting agent-based systems. - Nikolaos Evangelou; Dimitrios G. Giovanis; George A. Kevrekidis; Grigorios A. Pavliotis; Ioannis G. Kevrekidis (CoRR, 2023)

Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling. - Nikolaos Evangelou; Tianqi Cui; Juan M. Bello-Rivas; Alexei Makeev; Ioannis G. Kevrekidis (CoRR, 2023)

Double Diffusion Maps and their Latent Harmonics for scientific computations in latent space. - Nikolaos Evangelou; Felix Dietrich; Eliodoro Chiavazzo; Daniel Lehmberg; Marina Meila; Ioannis G. Kevrekidis (J. Comput. Phys., 2023)

From partial data to out-of-sample parameter and observation estimation with diffusion maps and geometric harmonics. - Eleni D. Koronaki; Nikolaos Evangelou; Yorgos M. Psarellis; Andreas G. Boudouvis; Ioannis G. Kevrekidis (Comput. Chem. Eng., 2023)

From Noise to Signal: Unveiling Treatment Effects from Digital Health Data through Pharmacology-Informed Neural-SDE. - Samira Pakravan; Nikolaos Evangelou; Maxime Usdin; Logan Brooks; James Lu (CoRR, 2024)

Comparing analytic and data-driven approaches to parameter identifiability: A power systems case study. - Nikolaos Evangelou; Alexander M. Stankovic; Ioannis G. Kevrekidis; Mark K. Transtrum (CoRR, 2024)

Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era. - Eleni D. Koronaki; Nikolaos Evangelou; Cristina P. Martin-Linares; Edriss S. Titi; Ioannis G. Kevrekidis (J. Comput. Phys., 2024)

Generative Learning for Slow Manifolds and Bifurcation Diagrams. - Ellis R. Crabtree; Dimitris G. Giovanis; Nikolaos Evangelou; Juan M. Bello-Rivas; Ioannis G. Kevrekidis (CoRR, 2025)

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps. - Dimitris G. Giovanis; Nikolaos Evangelou; Ioannis G. Kevrekidis; Roger G. Ghanem (CoRR, 2025)