4.8k citations · 16.2k across the 155 of their papers we have counts for
168 papers
SIPHy: Sparse identification of port-Hamiltonian systems from noisy data
Håkon Noren Myhr, Sølve Eidnes, J. Nathan Kutz
We propose sparse identification of port-Hamiltonian systems (SIPHy), enabling structure-preserving symbolic regression from noisy trajectory observations. The method applies to po…
Biophysics-informed deep operator learning for inverse problems with application to electrophysiological source reconstruction
Eardi Lila, Erica R. Peterson, Alexis N. Bosseler +2
Electrophysiological brain signals are typically acquired through indirect and noisy measurements, providing transformed representations of the underlying neural activity. Source r…
Stable Takens' Embedding Theorem for Non-Uniformly-Sampled Linear Systems
Fisher Ng, J. Nathan Kutz
Takens' time-delay embedding theorem provides conditions under which delay-coordinate maps, formed using uniformly-sampled time series of trajectories evolving on attractors of dyn…
Origins and mitigation of double descent in reduced order modeling
Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar
Latent low-dimensional structure in datasets of natural and engineered systems enables their sparse sensing, or full-state reconstruction from historical data and very few carefull…
Multi-Fidelity Learning with Shallow Recurrent Decoders for Multi-Physics Applications
Stefano Riva, Carolina Introini, J. Nathan Kutz +1
In reactor physics, neutronics and multi-physics phenomena can be modelled at different fidelity levels. High-fidelity models based on the Boltzmann transport equation, multi-group…
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…