Search Results for author: Krithika Manohar

Found 7 papers, 1 papers with code

PySensors: A Python Package for Sparse Sensor Placement

4 code implementations20 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.

Classification General Classification

Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning

no code implementations24 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.

Kernel Analog Forecasting: Multiscale Test Problems

no code implementations13 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.

Sparse Principal Component Analysis via Variable Projection

no code implementations1 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.

Optimized Sampling for Multiscale Dynamics

no code implementations14 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

Predicting shim gaps in aircraft assembly with machine learning and sparse sensing

no code implementations24 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.

Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification

no code implementations12 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.

General Classification Texture Classification +1

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