129 citations · 192 across the 9 of their papers we have counts for
11 papers
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, Sajeda Mokbel, Miro Gondrum +18
Modeling and controlling fluids is critical across science and engineering. Effective flow control can increase lift, reduce drag, enhance mixing, and attenuate noise, potentially…
Computed tomography using meta-optics
Maksym Zhelyeznuyakov, Johannes E. Fröch, Shane Colburn +2
Computer vision tasks require processing large amounts of data to perform image classification, segmentation, and feature extraction. Optical preprocessors can potentially reduce t…
A deep learning approach to wall-shear stress quantification: From numerical training to zero-shot experimental application
Esther Lagemann, Julia Roeb, Steven L. Brunton +1
The accurate quantification of wall-shear stress dynamics is of substantial importance for various applications in fundamental and applied research, spanning areas from human healt…
Statistics dependent spectral properties of random arrays of particles
Romil Audhkhasi, Maksym Zhelyeznyakov, Steven Brunton +1
The ability to tailor the spectral response of photonic devices is paramount to the advancement of a broad range of applications. The vast design space offered by disordered optica…
Large area optimization of meta-lens via data-free machine learning
Maksym V. Zhelyeznyakov, Johannes E. Froch, Anna Wirth-Singh +4
Sub-wavelength diffractive optics meta-optics present a multi-scale optical system, where the behavior of constituent sub-wavelength scatterers, or meta-atoms, need to be modelled…
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…
Optimal Sensor and Actuator Selection using Balanced Model Reduction
Krithika Manohar, J. Nathan Kutz, Steven L. Brunton
Optimal sensor and actuator selection is a central challenge in high-dimensional estimation and control. Nearly all subsequent control decisions are affected by these sensor/actuat…
Smoothing and parameter estimation by soft-adherence to governing equations
Samuel Rudy, Steven Brunton, J. Nathan Kutz
The analysis of high-dimensional dynamical systems generally requires the integration of simulation data with experimental measurements. Experimental data often has substantial amo…
Data-driven discovery of partial differential equations
Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor +1
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The…
Environment Identification in Flight using Sparse Approximation of Wing Strain
Krithika Manohar, Steven L. Brunton, J. Nathan Kutz
This paper addresses the problem of identifying different flow environments from sparse data collected by wing strain sensors. Insects regularly perform this feat using a sparse en…
Compressive sensing and low-rank libraries for classification of bifurcation regimes in nonlinear dynamical systems
Steven L. Brunton, Jonathan H. Tu, Ido Bright +1
We show that for complex nonlinear systems, model reduction and compressive sensing strategies can be combined to great advantage for classifying, projecting, and reconstructing th…