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At ASSIST Lab, we perform state-of-the-art research at the intersection of Astrodynamics, Space Weather and Machine Learning. We are working on developing novel technologies and solutions including methods, tools, and software for real-world problems in:
- Astrodynamics, Space Situational Awareness and Space Traffic Management
- Space Weather - Probabilistic Modeling and Forecasting with Uncertainty Quantification
- Physics-informed Machine Learning
- Space Exploration
- Atmospheric re-entry
- Dynamic Reduced Order Modeling for Large-Scale, High-Dimensional Systems
- Model-Data Fusion and Nonlinear State Estimation
- Data-Driven Modeling
For more information on research projects, publications, open-source and free software, the team, and sponsors of our research, please feel free to navigate the different pages.
News
A WVU team led by Dr. Yu Gu and Dr. Piyush Mehta will be working on the following project to test exciting new technology:
Inspired by spiders’ ballooning capabilities, the proposed concept envision the deployment of thousands of micro probes to study planetary atmospheres. Each micro probe, with a total mass of about 50 mg, will have a small payload pod hanging under a 200 m long string loop, which provides both atmospheric drag and electrostatic lifts. Two electric booms each about 2.5 m long will sense the Atmospheric Potential Gradient (APG) and harvest a small amount of electricity for powering the probe. The payload pod will contain energy storage and conversion devices, an actuator for replenishing and regulating the static electric charge on the string loop, and integrated microprocessor, radio, and sensors. The motions of the micro probes will be uncontrolled along the horizontal directions but can be regulated to a limited degree along the vertical direction. The onboard control system will allow extending the mission time and increasing the probability of keeping the probe in the desired atmospheric layer. If successful, the proposed micro probes can support several future planetary missions by gaining large spatial-scale atmospheric sensing capabilities. [Written by Dr. Yu Gu]