Sparse Recovery for Scientific Data
We have built build and analyzed fundamental mathematical tools that enable sparse recovery of scientific data for high performance computing, experimental facilities and sensor measurement. The proposed work will impact many areas of research across the DOE. We have designed, developed and analyzed fast scalable algorithms for the sparse recovery of scientific data sets that may be extremely large, generated very quickly and distributed across multiple storage devices. The algorithms we developed will be used to process the copious amounts of scientific data generated by the DOE supercomputer simulations and by DOE experimental and observational facilities. They leverage the fact that while the amount of data produced can be extremely large, the actual quantities of interest (QoI's) are often sparse, either in space time, or in some feature space. Mathematically, notions of compressive sensing, including l1 regularized and total variation regularized optimization have played an important part in this effort.
- Research Organization:
- Univ. of California, Los Angeles, CA (United States)
- Sponsoring Organization:
- USDOE Chicago Operations Office (CO); USDOE Office of Science (SC)
- DOE Contract Number:
- SC0013838
- OSTI ID:
- 1561286
- Report Number(s):
- DE-SC0013838F
- Resource Relation:
- Related Information: Block matching local SVD operator based sparsity and TV regularization for image denoisingJ Liu, S OsherJournal of Scientific Computing 78 (1), 607-624, 2018
- Country of Publication:
- United States
- Language:
- English
Similar Records
Cryo-STEM Tomography with Inpainting
TeaLeaf: A Mini-Application to Enable Design-Space Explorations for Iterative Sparse Linear Solvers