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Found 11 papers, 1 papers with code
Data-Driven Ergonomic Risk Assessment of Complex Hand-intensive Manufacturing Processes
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.
Statistical Mechanics of Dynamical System Identification
Recovering dynamical equations from observed noisy data is the central challenge of system identification.
Data-Induced Interactions of Sparse Sensors
Because of this structure, we can use just a few spatially localized sensor measurements to reconstruct the full state of a complex system.
Constrained optimization of sensor placement for nuclear digital twins
Strategically placing sensors within defined spatial constraints is essential for the reconstruction of reactor flow fields and the creation of nuclear digital twins.
PySensors: A Python Package for Sparse Sensor Placement
PySensors is a Python package for selecting and placing a sparse set of sensors for classification and reconstruction tasks.
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning
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
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
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
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
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
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.
