works on

From the 1 of 12 linked papers with an AI index.

collaborators

12 papers

stat.ML2026

Origins and mitigation of double descent in reduced order modeling

Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar

The paper investigates why double descent errors occur in reduced‑order models and proposes a data‑noise averaging framework to predict and mitigate these instabilities, demonstrat…

cs.LG2026

Real-time optimal control with shallow recurrent decoder networks

Matteo Tomasetto, Francesco Braghin, J. Nathan Kutz +1

Controlling dynamical systems in real-time across multiple scenarios is critical to enabling adaptive control strategies, ensuring stability and efficiency. However, to tailor cont…

math.DS2026

PRONE: Petrov-Galerkin Operator Learning Unifies DMD, SINDy & Koopmanism

Matthew J. Colbrook, April Herwig, J. Nathan Kutz

Data-driven dynamics often asks how to linearize a nonlinear system. We ask instead: which observables should be advanced, and where should their futures live? This leads to Petrov…

cond-mat.dis-nn2026

Signature of glassy dynamics in dynamic modes decompositions

Zachary G. Nicolaou, Hangjun Cho, Yuanzhao Zhang +2

Glasses are traditionally characterized by their rugged landscape of disordered low-energy states and their slow relaxation towards thermodynamic equilibrium. Far from equilibrium,…

q-bio.QM2026

An Interpretable Data-Driven Model of the Flight Dynamics of Hawks

Lydia France, Karl Lapo, J. Nathan Kutz

Despite significant analysis of bird flight, generative physics models for flight dynamics do not currently exist. Yet the underlying mechanisms responsible for various flight mano…

physics.plasm-ph2026

Data-driven methods to discover stable linear models of the helicity injectors on HIT-SIU

Zachary L. Daniel, Alan A. Kaptanoglu, Christopher J. Hansen +3

Accurate and efficient circuit models are necessary to control the power electronic circuits found on plasma physics experiments. Tuning and controlling the behavior of these circu…