151 citations · 277 across the 8 of their papers we have counts for
9 papers
LAtent Phase Inference from Short time sequences using SHallow REcurrent Decoders (LAPIS-SHRED)
Yuxuan Bao, Xingyue Zhang, J. Nathan Kutz
Reconstructing full spatio-temporal dynamics from sparse observations in both space and time remains a central challenge in complex systems, as measurements can be spatially incomp…
Dimensionally Consistent Learning with Buckingham Pi
Joseph Bakarji, Jared Callaham, Steven L. Brunton +1
In the absence of governing equations, dimensional analysis is a robust technique for extracting insights and finding symmetries in physical systems. Given measurement variables an…
Modeling Thermodynamic Trends of Rotating Detonation Engines
James Koch, J. Nathan Kutz
The formation of a number of co- and counter-rotating coherent combustion wave fronts is the hallmark feature of the Rotating Detonation Engine (RDE). The engineering implications…
Multi-fidelity sensor selection: Greedy algorithms to place cheap and expensive sensors with cost constraints
Emily Clark, Steven L. Brunton, J. Nathan Kutz
We develop greedy algorithms to approximate the optimal solution to the multi-fidelity sensor selection problem, which is a cost constrained optimization problem prescribing the pl…
Sensor Selection With Cost Constraints for Dynamically Relevant Bases
Emily Clark, J. Nathan Kutz, Steven L. Brunton
We consider cost-constrained sparse sensor selection for full-state reconstruction, applying a well-known greedy algorithm to dynamical systems for which the usual singular value d…
Mode-Locked Rotating Detonation Waves: Experiments and a Model Equation
James Koch, Mitsuru Kurosaka, Carl Knowlen +1
Direct observation of a Rotating Detonation Engine combustion chamber has enabled the extraction of the kinematics of its detonation waves. These records exhibit a rich set of inst…
A unified sparse optimization framework to learn parsimonious physics-informed models from data
Kathleen Champion, Peng Zheng, Aleksandr Y. Aravkin +2
Machine learning (ML) is redefining what is possible in data-intensive fields of science and engineering. However, applying ML to problems in the physical sciences comes with a uni…
A Unified Framework for Sparse Relaxed Regularized Regression: SR3
Peng Zheng, Travis Askham, Steven L. Brunton +2
Regularized regression problems are ubiquitous in statistical modeling, signal processing, and machine learning. Sparse regression in particular has been instrumental in scientific…
Sparse Identification of Nonlinear Dynamics for Rapid Model Recovery
Markus Quade, Markus Abel, J. Nathan Kutz +1
Big data has become a critically enabling component of emerging mathematical methods aimed at the automated discovery of dynamical systems, where first principles modeling may be i…