115 citations · 381 across the 41 of their papers we have counts for
5 papers · 1 filter
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series data
Daniel E. Shea, Rajiv Giridharagopal, David S. Ginger +2
Time-series analysis is critical for a diversity of applications in science and engineering. By leveraging the strengths of modern gradient descent algorithms, the Fourier transfor…
PySensors: A Python Package for Sparse Sensor Placement
Brian M. de Silva, Krithika Manohar, Emily Clark +3
PySensors is a Python package for selecting and placing a sparse set of sensors for classification and reconstruction tasks. Specifically, PySensors implements algorithms for data-…
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data
Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz
The sparse identification of nonlinear dynamics (SINDy) is a regression framework for the discovery of parsimonious dynamic models and governing equations from time-series data. As…
Deep reinforcement learning for optical systems: A case study of mode-locked lasers
Chang Sun, Eurika Kaiser, Steven L. Brunton +1
We demonstrate that deep reinforcement learning (deep RL) provides a highly effective strategy for the control and self-tuning of optical systems. Deep RL integrates the two leadin…
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…