518 citations · 877 across the 16 of their papers we have counts for
20 papers
Origins and mitigation of double descent in reduced order modeling
Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar
Latent low-dimensional structure in datasets of natural and engineered systems enables their sparse sensing, or full-state reconstruction from historical data and very few carefull…
OpenReservoirComputing: GPU-Accelerated Reservoir Computing in JAX
Jan Williams, Dima Tretiak, Steven L. Brunton +2
OpenReservoirComputing (ORC) is a Python library for reservoir computing (RC) written in JAX (Bradbury et al. 2018) and Equinox (Kidger and Garcia 2021). JAX is a Python library fo…
Online Kernel Dynamic Mode Decomposition for Streaming Time Series Forecasting with Adaptive Windowing
Christopher Salazar, Krithika Manohar, Ashis G. Banerjee
Real-time forecasting from streaming data poses critical challenges: handling non-stationary dynamics, operating under strict computational limits, and adapting rapidly without cat…
PySensors 2.0: A Python Package for Sparse Sensor Placement
Niharika Karnik, Yash Bhangale, Mohammad G. Abdo +6
PySensors is a Python package for selecting and placing a sparse set of sensors for reconstruction and classification tasks. In this major update to PySensors, we introduce spatial…
Reservoir computing for system identification and predictive control with limited data
Jan P. Williams, J. Nathan Kutz, Krithika Manohar
Model predictive control (MPC) is an industry standard control technique that iteratively solves an open-loop optimization problem to guide a system towards a desired state or traj…
Data-Driven Ergonomic Risk Assessment of Complex Hand-intensive Manufacturing Processes
Anand Krishnan, Xingjian Yang, Utsav Seth +8
Hand-intensive manufacturing processes, such as composite layup and textile draping, require significant human dexterity to accommodate task complexity. These strenuous hand motion…
Statistical Mechanics of Dynamical System Identification
Andrei A. Klishin, Joseph Bakarji, J. Nathan Kutz +1
Recovering dynamical equations from observed noisy data is the central challenge of system identification. We develop a statistical mechanics approach to analyze sparse equation di…
Extending the trapping theorem to provide local stability guarantees for quadratically nonlinear models
Mai Peng, Alan Kaptanoglu, Chris Hansen +3
The Navier Stokes equations (NSEs) are partial differential equations (PDEs) to describe the nonlinear convective motion of fluids and they are computationally expensive to simulat…
Data-Induced Interactions of Sparse Sensors Using Statistical Physics
Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar
Large-dimensional empirical data in science and engineering frequently have a low-rank structure and can be represented as a combination of just a few eigenmodes. Because of this s…
Constrained optimization of sensor placement for nuclear digital twins
Niharika Karnik, Mohammad G. Abdo, Carlos E. Estrada Perez +6
The deployment of extensive sensor arrays in nuclear reactors is infeasible due to challenging operating conditions and inherent spatial limitations. Strategically placing sensors…
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-…
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning
Steven L. Brunton, J. Nathan Kutz, Krithika Manohar +9
Data science, and machine learning in particular, is rapidly transforming the scientific and industrial landscapes. The aerospace industry is poised to capitalize on big data and m…
Kernel Analog Forecasting: Multiscale Test Problems
Dmitry Burov, Dimitrios Giannakis, Krithika Manohar +1
Data-driven prediction is becoming increasingly widespread as the volume of data available grows and as algorithmic development matches this growth. The nature of the predictions m…
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…
Sparse Principal Component Analysis via Variable Projection
N. Benjamin Erichson, Peng Zheng, Krithika Manohar +3
Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying loc…
Optimized Sampling for Multiscale Dynamics
Krithika Manohar, Eurika Kaiser, Steven L. Brunton +1
The characterization of intermittent, multiscale and transient dynamics using data-driven analysis remains an open challenge. We demonstrate an application of the Dynamic Mode Deco…
Predicting shim gaps in aircraft assembly with machine learning and sparse sensing
Krithika Manohar, Thomas Hogan, Jim Buttrick +3
A modern aircraft may require on the order of thousands of custom shims to fill gaps between structural components in the airframe that arise due to manufacturing tolerances adding…
Randomized CP Tensor Decomposition
N. Benjamin Erichson, Krithika Manohar, Steven L. Brunton +1
The CANDECOMP/PARAFAC (CP) tensor decomposition is a popular dimensionality-reduction method for multiway data. Dimensionality reduction is often sought after since many high-dimen…
Data-Driven Sparse Sensor Placement for Reconstruction
Krithika Manohar, Bingni W. Brunton, J. Nathan Kutz +1
Optimal sensor placement is a central challenge in the design, prediction, estimation, and control of high-dimensional systems. High-dimensional states can often leverage a latent…
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…