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Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part II -- Clustering Extremely High-Dimensional Grid-Based Data
Machine Learning · Submitted 3 Jul 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our approach makes clustering of extremely high dimensional data tractable, generating meaningful cluster assignments for our problem, despite the approximation introduced in the random projections.
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The abstract, sentence by sentence
Building an accurate surrogate model for the spatio-temporal outputs of a computer simulation is a challenging task. A simple approach to improve the accuracy of the surrogate is to cluster the outputs based on similarity and build a separate surrogate model for each cluster. This clustering is relatively straightforward when the output at each time step is of moderate size. However, when the spatial domain is represented by a large number of grid points, numbering in the millions, the clustering of the data becomes more challenging. In this report, we consider output data from simulations of a jet interacting with high explosives. These data are available on spatial domains of different sizes, at grid points that vary in their spatial coordinates, and in a format that distributes the output across multiple files at each time step of the simulation. We first describe how we bring these data into a consistent format prior to clustering. Borrowing the idea of random projections from data mining, we reduce the dimension of our data by a factor of thousand, making it possible to use the iterative k-means method for clustering. We show how we can use the randomness of both the random projections, and the choice of initial centroids in k-means clustering, to determine the number of clusters in our data set. Our approach makes clustering of extremely high dimensional data tractable, generating meaningful cluster assignments for our problem, despite the approximation introduced in the random projections.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Building an accurate surrogate model for the spatio-temporal outputs of a computer simulation is a challenging task. | background 1.00 | 0.06 | 0.03 | asserted 1.00 |
| s1 | A simple approach to improve the accuracy of the surrogate is to cluster the outputs based on similarity and build a separate surrogate model for each cluster. | background 0.69 | 0.10 | 0.05 | asserted 0.80 |
| s2 | This clustering is relatively straightforward when the output at each time step is of moderate size. | background 0.98 | 0.10 | 0.02 | asserted 1.00 |
| s3 | However, when the spatial domain is represented by a large number of grid points, numbering in the millions, the clustering of the data becomes more challenging. | background 0.79 | 0.09 | 0.03 | asserted 0.99 |
| s4 | In this report, we consider output data from simulations of a jet interacting with high explosives. | background 0.50 | 0.45 | 0.02 | asserted 0.98 |
| s5 | These data are available on spatial domains of different sizes, at grid points that vary in their spatial coordinates, and in a format that distributes the output across multiple files at each time step of the simulation. | background 0.71 | 0.44 | 0.02 | asserted 1.00 |
| s6 | We first describe how we bring these data into a consistent format prior to clustering. | method 0.82 | 0.85 | 0.02 | asserted 0.78 |
| s7 | Borrowing the idea of random projections from data mining, we reduce the dimension of our data by a factor of thousand, making it possible to use the iterative k-means method for clustering. | method 0.98 | 0.92 | 0.13 | asserted 0.99 |
| s8 | We show how we can use the randomness of both the random projections, and the choice of initial centroids in k-means clustering, to determine the number of clusters in our data set. | method 0.86 | 0.92 | 0.10 | asserted 0.97 |
| s9 | Our approach makes clustering of extremely high dimensional data tractable, generating meaningful cluster assignments for our problem, despite the approximation introduced in the random projections. | result 0.59 | 0.94 | 0.80 | asserted 1.00 |
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The paper
Original abstract
Building an accurate surrogate model for the spatio-temporal outputs of a computer simulation is a challenging task. A simple approach to improve the accuracy of the surrogate is to cluster the outputs based on similarity and build a separate surrogate model for each cluster. This clustering is relatively straightforward when the output at each time step is of moderate size. However, when the spatial domain is represented by a large number of grid points, numbering in the millions, the clustering of the data becomes more challenging. In this report, we consider output data from simulations of a jet interacting with high explosives. These data are available on spatial domains of different sizes, at grid points that vary in their spatial coordinates, and in a format that distributes the output across multiple files at each time step of the simulation. We first describe how we bring these data into a consistent format prior to clustering. Borrowing the idea of random projections from data mining, we reduce the dimension of our data by a factor of thousand, making it possible to use the iterative k-means method for clustering. We show how we can use the randomness of both the random projections, and the choice of initial centroids in k-means clustering, to determine the number of clusters in our data set. Our approach makes clustering of extremely high dimensional data tractable, generating meaningful cluster assignments for our problem, despite the approximation introduced in the random projections.
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