Speaker
Description
Modeling and simulation of real-world applications often involve dynamical systems with large degrees of freedom, requiring substantial computational time and resources. Projection-based model reduction enables efficient simulation of such dynamical systems by constructing low-dimensional surrogate models from high-dimensional data. Specifically, Operator Inference (OpInf) learns such reduced surrogate models through a two-step process: constructing a low-dimensional basis via Singular Value Decomposition (SVD) to compress the data, then solving a linear least-squares (LS) problem to infer reduced operators that govern the dynamics in this compressed space, all without access to the underlying code or full model operators, i.e., non-intrusively. Traditional OpInf operates as a batch learning method, where both the SVD and LS steps process all data simultaneously, which limits scalability to large-scale applications generating terabytes to petabytes of data and prevents real-time model updates in online scenarios. To address these limitations, we propose Streaming OpInf, which learns reduced models incrementally as snapshot data arrives. Our method employs prominent streaming algorithms from numerical linear algebra: incremental SVD for adaptive basis construction and recursive LS for streaming operator updates, eliminating the need to store complete datasets while enabling online model adaptation. We systematically compare multiple streaming algorithm variants to identify effective combinations for accurate reduced model learning. A Numerical experiment on a large-scale turbulent channel flow with a friction Reynolds number of $Re_\tau = 5200$ demonstrate that Streaming OpInf achieves accuracy comparable to batch OpInf while reducing memory requirements significantly and enabling dimension reductions exceeding 31,000x, resulting in orders-of-magnitude faster predictions.