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20202023
most citedMultifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders

3 citations · 6 across the 5 of their papers we have counts for

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cs.LG2023

Small-data Reduced Order Modeling of Chaotic Dynamics through SyCo-AE: Synthetically Constrained Autoencoders

Andrey A. Popov, Renato Zanetti

Data-driven reduced order modeling of chaotic dynamics can result in systems that either dissipate or diverge catastrophically. Leveraging non-linear dimensionality reduction of au…

cs.LG2021

Investigation of Nonlinear Model Order Reduction of the Quasigeostrophic Equations through a Physics-Informed Convolutional Autoencoder

Rachel Cooper, Andrey A. Popov, Adrian Sandu

Reduced order modeling (ROM) is a field of techniques that approximates complex physics-based models of real-world processes by inexpensive surrogates that capture important dynami…