no code implementations • 12 Jan 2017 • Wei Guo, Krithika Manohar, Steven L. Brunton, Ashis G. Banerjee
Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples.
no code implementations • 24 Nov 2017 • Krithika Manohar, Thomas Hogan, Jim Buttrick, Ashis G. Banerjee, J. Nathan Kutz, Steven L. Brunton
This new approach is based on the assumption that patterns exist in shim distributions across aircraft, which may be mined and used to reduce the burden of data collection and processing in future aircraft.
no code implementations • 14 Dec 2017 • Krithika Manohar, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
The multiresolution DMD is capable of characterizing nonlinear dynamical systems in an equation-free manner by recursively decomposing the state of the system into low-rank spatial modes and their temporal Fourier dynamics.
Dynamical Systems Numerical Analysis Data Analysis, Statistics and Probability
no code implementations • 1 Apr 2018 • N. Benjamin Erichson, Peng Zheng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin
Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying localized spatial structures in the data and disambiguating between distinct time scales.
no code implementations • 13 May 2020 • Dmitry Burov, Dimitrios Giannakis, Krithika Manohar, Andrew Stuart
The nature of the predictions made, and the manner in which they should be interpreted, depends crucially on the extent to which the variables chosen for prediction are Markovian, or approximately Markovian.
no code implementations • 24 Aug 2020 • Steven L. Brunton, J. Nathan Kutz, Krithika Manohar, Aleksandr Y. Aravkin, Kristi Morgansen, Jennifer Klemisch, Nicholas Goebel, James Buttrick, Jeffrey Poskin, Agnes Blom-Schieber, Thomas Hogan, Darren McDonald
Indeed, emerging methods in machine learning may be thought of as data-driven optimization techniques that are ideal for high-dimensional, non-convex, and constrained, multi-objective optimization problems, and that improve with increasing volumes of data.
4 code implementations • 20 Feb 2021 • Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz
PySensors is a Python package for selecting and placing a sparse set of sensors for classification and reconstruction tasks.
no code implementations • 23 Jun 2023 • Niharika Karnik, Mohammad G. Abdo, Carlos E. Estrada Perez, Jun Soo Yoo, Joshua J. Cogliati, Richard S. Skifton, Pattrick Calderoni, Steven L. Brunton, Krithika Manohar
Strategically placing sensors within defined spatial constraints is essential for the reconstruction of reactor flow fields and the creation of nuclear digital twins.
no code implementations • 21 Jul 2023 • Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar
Because of this structure, we can use just a few spatially localized sensor measurements to reconstruct the full state of a complex system.
no code implementations • 4 Mar 2024 • Andrei A. Klishin, Joseph Bakarji, J. Nathan Kutz, Krithika Manohar
Recovering dynamical equations from observed noisy data is the central challenge of system identification.
no code implementations • 5 Mar 2024 • Anand Krishnan, Xingjian Yang, Utsav Seth, Jonathan M. Jeyachandran, Jonathan Y. Ahn, Richard Gardner, Samuel F. Pedigo, Adriana, Blom-Schieber, Ashis G. Banerjee, Krithika Manohar
We develop a data-driven ergonomic risk assessment system with a special focus on hand and finger activity to better identify and address ergonomic issues related to hand-intensive manufacturing processes.