ASSIST Lab - What we do
Welcome to ASSIST Lab, where cutting-edge research at the intersection of Astrodynamics, Space Weather, and Machine Learning is our passion. Our dedicated team is committed to developing innovative methodologies and solutions that address real-world challenges in various domains, including
- Astrodynamics, Space Situational Awareness, and Space Traffic Management: Astrodynamics involves the study of spacecraft and celestial objects' motion in space. At ASSIST Lab, we focus on improving Space Situational Awareness (SSA) to better understand the positions and movements of objects in Earth's orbit, reducing the risk of collisions and enhancing space traffic management. Our research aims to develop advanced algorithms and methods for precise orbit determination, collision avoidance, and efficient satellite operations.
- Space Weather - Probabilistic Modeling and Forecasting with Uncertainty Quantification: Space Weather refers to the dynamic and complex conditions in space, including solar activity and geomagnetic storms, which can impact satellites, communication systems, and power grids on Earth. We concentrate on developing probabilistic models and forecasting techniques that account for uncertainty, allowing us to predict space weather events more accurately. These forecasts play a crucial role in safeguarding space-based and terrestrial technologies from potentially harmful space weather effects.
- Physics-informed Machine Learning: Integrating domain knowledge and physical principles into machine learning algorithms is essential for addressing complex problems effectively. Our research in Physics-informed Machine Learning explores methods that combine data-driven approaches with the laws of physics, enabling the development of more reliable and interpretable models for various space-related applications.
- Space Exploration: At ASSIST Lab, we are enthusiastic about pushing the boundaries of space exploration. Our research focuses on developing new spacecraft technologies, propulsion systems, and mission planning strategies to expand our understanding of the universe and potentially support human space exploration endeavors.
- Atmospheric Re-entry: When spacecraft and satellites re-enter Earth's atmosphere, they experience extreme conditions that require careful analysis and design. Our research in this area involves studying aerodynamics, thermal dynamics, and materials that enable safe and controlled atmospheric re-entry for spacecraft and debris.
- Dynamic Reduced Order Modeling for Large-Scale, High-Dimensional Systems: Large-scale systems, such as space missions and complex astrophysical processes, often involve numerous variables and complex interactions. To efficiently simulate and analyze these systems, we work on dynamic reduced-order modeling techniques, which aim to capture essential system behavior while significantly reducing computational complexity.
- Model-Data Fusion and Nonlinear State Estimation: Combining mathematical models with real-world data is vital for accurate system analysis and prediction. Our research focuses on state estimation techniques that fuse models and observations to determine the most likely state of a system, even in the presence of nonlinearities and uncertainties.
- Data-Driven Modeling: In the era of big data, we harness the power of large datasets to create data-driven models that can reveal hidden patterns, correlations, and trends. Our data-driven modeling research helps us gain insights into complex space-related phenomena and supports decision-making in space missions, satellite operations, and other aerospace applications.
If you're interested in learning more about our research projects, publications, open-source software, our talented team, or our esteemed sponsors, feel free to explore the different pages on our website.