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)

MarkerMap: nonlinear marker selection for single-cell studies. - Nabeel Sarwar; Wilson Gregory; George A. Kevrekidis; Soledad Villar; Bianca Dumitrascu (CoRR, 2022)

The passive symmetries of machine learning. - Soledad Villar; David W. Hogg; Weichi Yao; George A. Kevrekidis; Bernhard Schölkopf (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)

Conformal Disentanglement: A Neural Framework for Perspective Synthesis and Differentiation. - George A. Kevrekidis; Eleni D. Koronaki; Yannis G. Kevrekidis (CoRR, 2024)

Thinner Latent Spaces: Detecting dimension and imposing invariance through autoencoder gradient constraints. - George A. Kevrekidis; Mauro Maggioni; Soledad Villar; Yannis G. Kevrekidis (CoRR, 2024)

Towards fully covariant machine learning. - Soledad Villar; David W. Hogg; Weichi Yao; George A. Kevrekidis; Bernhard Schölkopf (Trans. Mach. Learn. Res., 2024)

Data-Driven, ML-assisted Approaches to Problem Well-Posedness. - Tom Bertalan; George A. Kevrekidis; Eleni D. Koronaki; Siddhartha Mishra; Elizaveta Rebrova; Yannis G. Kevrekidis (CoRR, 2025)

Towards Coordinate- and Dimension-Agnostic Machine Learning for Partial Differential Equations. - Trung V. Phan; George A. Kevrekidis; Soledad Villar; Yannis G. Kevrekidis; Juan M. Bello-Rivas (CoRR, 2025)