Speaker
Description
The exponential growth of scientific data from simulations and experiments demands efficient compression techniques for storage and processing. This talk introduces a novel streaming weak-SINDy algorithm designed for real-time compression of streaming scientific data. Leveraging the underlying structure of physical systems, the algorithm constructs memory-efficient feature matrices and target vectors in real-time, enabling model recovery in an offline regression stage. For high-dimensional data, we integrate a streaming proper orthogonal decomposition (POD) process, dynamically reducing data dimensions and augmenting the streaming weak-SINDy algorithm to handle evolving POD bases. Proof-of-concept examples, including applications to the Lorenz system and fluid-flow data, demonstrate both the memory efficiency and reconstruction accuracy of the approach. Join us to explore how these advancements pave the way for scalable, real-time compression of scientific data.