32 citations · 46 across the 5 of their papers we have counts for
6 papers
Mollified-sharp decomposition: a probabilistic regularization of parametric POD for shock-bearing flows
Oliver T. Schmidt
This paper introduces the mollified-sharp decomposition, a probabilistic regularization of moving shocks in parametric reduced-order models. Each detected shock location is treated…
Boundary Layer Transition as Succession of Temporal and Spatial Symmetry Breaking
Cong Lin, Oliver T. Schmidt
We show that both temporal and spatial symmetry breaking in canonical K-type transition arise as organized hydrodynamic structures rather than stochastic fluctuations. Before the s…
Data-Driven Forecasting of High-Dimensional Transient and Stationary Processes via Space-Time Projection
Oliver T. Schmidt
Space-Time Projection (STP) is introduced as a data-driven forecasting approach for high-dimensional and time-resolved data. The method computes extended space-time proper orthogon…
Adaptive spectral proper orthogonal decomposition of tonal flows
Brandon C. Y. Yeung, Oliver T. Schmidt
An adaptive algorithm for spectral proper orthogonal decomposition (SPOD) of mixed broadband-tonal turbulent flows is developed. Sharp peak resolution at tonal frequencies is achie…
Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package
Marcin Rogowski, Brandon C. Y. Yeung, Oliver T. Schmidt +4
We propose a parallel (distributed) version of the spectral proper orthogonal decomposition (SPOD) technique. The parallel SPOD algorithm distributes the spatial dimension of the d…
Neural-network learning of SPOD latent dynamics
Andrea Lario, Romit Maulik, Oliver T. Schmidt +2
We aim to reconstruct the latent space dynamics of high dimensional, quasi-stationary systems using model order reduction via the spectral proper orthogonal decomposition (SPOD). T…