M2TD: Multi-Task Tensor Decomposition for Sparse Ensemble Simulations
| Main Authors: | Li, Xinsheng, Candan, Kasim Selcuk, Sapino, Maria Luisa |
|---|---|
| Format: | Proceeding |
| Bahasa: | eng |
| Terbitan: |
, 2018
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| Online Access: |
https://zenodo.org/record/3676685 |
Daftar Isi:
- Data- and model-driven computer simulations are increasingly critical in many application domains. These simulations may track 10s or 100s of parameters, affected by complex inter-dependent dynamic processes. Moreover, decision makers usually need to run large simulation ensembles, containing 1000s of simulations. In this paper, we rely on a tensor-based framework to represent and analyze patterns in large simulation ensemble data sets to obtain a high-level understanding of the dynamic processes implied by a given ensemble of simulations.We, further, note that the inherent sparsity of the simulation ensembles (relative to the space of potential simulations one can run) constitutes a significant problem in discovering these underlying patterns. To address this challenge, we propose a partition-stitch sampling scheme, which divides the parameter space into subspaces to collect several lower modal ensembles, and complement this with a novel Multi-Task Tensor Decomposition (M2TD), technique which helps effectively and efficiently stitch these subensembles back. Experiments showed that, for a given budget of simulations, the proposed structured sampling scheme leads to significantly better overall accuracy relative to traditional sampling approaches, even when the user does not have perfect information to help guide the structured partitioning process.