Deep Learning Based Basic and Applied Research
For the success of International Big Science Projects that concern scientific experiments using gravitational-wave detectors and the like, NIMS has collected scientific data through collaborative research among domestic and overseas researchers and performed research and development of data analysis methods for scientific experiment based on deep learning. To achieve new and groundbreaking scientific outcomes, many international science experiment projects are becoming larger in scale and the volumes of experimental data produced from such efforts are becoming gargantuan. This is the reason why deep learning is increasingly finding an important role in the analysis of scientific data.
Key Research Content
- Deep learning applications to the data analysis of LIGO gravitational-wave detector data, an International Big Science Project
- Research of data analysis methods and the mitigation of gravity gradient noise during next generation gravitational-wave
- Development of non-linear correlation catching algorithms for the detection of anomaly and noise source of time series data
- Scientific research on gravitational-wave through international collaboration with KAGRA, Virgo, LIGO and research institutions and universities in Korea