1 citations · 1 across the 4 of their papers we have counts for
4 papers
Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks
Tomoki Koike, Prakash Mohan, Marc T. Henry de Frahan +2
Linear dimensionality reduction methods such as proper orthogonal decomposition (POD) make high-dimensional data amenable to analysis by identifying the principal components, or mo…
Streaming Operator Inference for Model Reduction of Large-Scale Dynamical Systems
Tomoki Koike, Prakash Mohan, Marc T. Henry de Frahan +2
Projection-based model reduction enables efficient simulation of complex dynamical systems by constructing low-dimensional surrogate models from high-dimensional data. The Operator…
Operator Inference Aware Quadratic Manifolds with Isotropic Reduced Coordinates for Nonintrusive Model Reduction
Paul Schwerdtner, Prakash Mohan, Julie Bessac +2
Quadratic manifolds for nonintrusive reduced modeling are typically trained to minimize the reconstruction error on snapshot data, which means that the error of models fitted to th…
Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction
Paul Schwerdtner, Prakash Mohan, Aleksandra Pachalieva +3
This work introduces an online greedy method for constructing quadratic manifolds from streaming data, designed to enable in-situ analysis of numerical simulation data on the Petab…