Machine learning for scientific discovery with examples in fluid mechanics
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Machine learning may be used to develop accurate and efficient nonlinear dynamical systems models for complex natural and engineered systems. This AI for Good Webinar curated by the International Atomic Energy Agency (IAEA) explores the sparse identification of nonlinear dynamics (SINDy) algorithm, which identifies a minimal dynamical system model that balances model complexity with accuracy, avoiding overfitting. This approach tends to promote models that are interpretable and generalizable, capturing the essential “physics” of the system. It will also discuss the importance of learning effective coordinate systems in which the dynamics may be expected to be sparse. This sparse modeling approach will be demonstrated on a range of challenging modeling problems in fluid dynamics, and will discuss how to incorporate these models into existing model-based control efforts. Because fluid dynamics is central to fields such as transportation and health, it will emphasize the importance of machine learning solutions that are interpretable, explainable, generalizable, and that respect known physics.
This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.