Engineering structural robustness in power grid networks susceptible to community desynchronization. - Daniel Dylewsky; Xiu Yang; Alexandre M. Tartakovsky; J. Nathan Kutz (Appl. Netw. Sci., 2019)

Data-driven multiscale decompositions for forecasting and model discovery. - Daniel Dylewsky; Molei Tao; J. Nathan Kutz (CoRR, 2019)

Principal Component Trajectories (PCT): Nonlinear dynamics as a superposition of time-delayed periodic orbits. - Daniel Dylewsky; Eurika Kaiser; Steven L. Brunton; J. Nathan Kutz (CoRR, 2020)

Sparse Identification of Slow Timescale Dynamics. - Jason J. Bramburger; Daniel Dylewsky; J. Nathan Kutz (CoRR, 2020)

Dynamic mode decomposition for forecasting and analysis of power grid load data. - Daniel Dylewsky; David Barajas-Solano; Tong Ma; Alexandre M. Tartakovsky; J. Nathan Kutz (CoRR, 2020)

Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems. - Daniel Dylewsky; David Barajas-Solano; Tong Ma; Alexandre M. Tartakovsky; J. Nathan Kutz (IEEE Access, 2022)

Universal Early Warning Signals of Phase Transitions in Climate Systems. - Daniel Dylewsky; Timothy M. Lenton; Marten Scheffer; Thomas M. Bury; Christopher G. Fletcher; Madhur Anand; Chris T. Bauch (CoRR, 2022)

Predicting discrete-time bifurcations with deep learning. - Thomas M. Bury; Daniel Dylewsky; Chris T. Bauch; Madhur Anand; Leon Glass; Alvin Shrier; Gil Bub (CoRR, 2023)

Neural models for prediction of spatially patterned phase transitions: methods and challenges. - Daniel Dylewsky; Sonia Kéfi; Madhur Anand; Chris T. Bauch (CoRR, 2025)