Seminar: Integrating Machine Learning with Space Research and Technology (II)
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Scientific research has recently evolved by performing data-driven modeling via machine learning (ML) techniques. ML is used to make machines "intelligent" by allowing them to learn from data (i.e. experience). Integrating ML in space research can open new frontiers, revolutionizing data analysis and improving our understanding. However, challenges persist in deploying ML in space research, including the need for robust algorithms, data limitations, and the interpretability of AI-driven decisions in critical scenarios. As advancements continue, ML and space exploration synergy promises groundbreaking discoveries and enhanced capabilities for humanity's endeavors beyond Earth's boundaries. In this talk, I will outline the basic understanding of ML algorithms and how those algorithms can be incorporated into space research and technology.
This talk will explore the multifaceted applications of ML in various domains of space exploration, for example:
1) Light-curve classification from Satellite Laser Ranging data
2) Chemical network prediction for planet-forming disks
3) ML technology for exoplanet weather forecasts
4) ML-based data compression algorithm for on-board computers
5) ML for exoplanet characterization and evolution