Goal-Oriented Low-Rank Tensor Decompositions for Numerical Simulation Data
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918125560135680 |
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| author | Dunlavy, Daniel M. Phipps, Eric T. Kolla, Hemanth Shadid, John N. Phillips, Edward |
| author_facet | Dunlavy, Daniel M. Phipps, Eric T. Kolla, Hemanth Shadid, John N. Phillips, Edward |
| contents | We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between the model data and simulation data as well as functions of the model data and functions of the simulation data. This novel approach to dimensionality reduction of simulation data provides a means of directly incorporating quantities of interests and invariants associated with conservation principles associated with the simulation data into the low-dimensional model, thus enabling more accurate analysis of the simulation without requiring access to the full set of high-dimensional data. Computational results of applying this approach to two standard low-rank tensor decompositions of data arising from simulation of combustion and plasma physics are presented. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11139 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Goal-Oriented Low-Rank Tensor Decompositions for Numerical Simulation Data Dunlavy, Daniel M. Phipps, Eric T. Kolla, Hemanth Shadid, John N. Phillips, Edward Numerical Analysis 15A69, 65F55 We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between the model data and simulation data as well as functions of the model data and functions of the simulation data. This novel approach to dimensionality reduction of simulation data provides a means of directly incorporating quantities of interests and invariants associated with conservation principles associated with the simulation data into the low-dimensional model, thus enabling more accurate analysis of the simulation without requiring access to the full set of high-dimensional data. Computational results of applying this approach to two standard low-rank tensor decompositions of data arising from simulation of combustion and plasma physics are presented. |
| title | Goal-Oriented Low-Rank Tensor Decompositions for Numerical Simulation Data |
| topic | Numerical Analysis 15A69, 65F55 |
| url | https://arxiv.org/abs/2508.11139 |