18 citations · 22 across the 5 of their papers we have counts for
5 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…
Physics-Informed Machine Learning for Characterizing System Stability
Tomoki Koike, Elizabeth Qian
In the design and operation of complex dynamical systems, it is essential to ensure that all state trajectories of the dynamical system converge to a desired equilibrium within a g…
Energy-Preserving Reduced Operator Inference for Efficient Design and Control
Tomoki Koike, Elizabeth Qian
Many-query computations, in which a computational model for an engineering system must be evaluated many times, are crucial in design and control. For systems governed by partial d…
Splash Detail Due to Grain Incident on Granular Bed
Takahiro Tanabe, Tomoki Koike, Takashi Shimada +2
Using the discrete element method (DEM), we study the splash processes induced by the impact of a grain on two types of granular beds, namely, randomly packed and FCC-structured be…