6 papers
UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression
Mars Liyao Gao, Yuxuan Bao, Amy S. Rude +2
Reconstructing high-dimensional spatiotemporal fields from sparse sensor measurements is critical in a wide range of scientific applications. The SHallow REcurrent Decoder (SHRED)…
LAtent Phase Inference from Short time sequences using SHallow REcurrent Decoders (LAPIS-SHRED)
Yuxuan Bao, Xingyue Zhang, J. Nathan Kutz
Reconstructing full spatio-temporal dynamics from sparse observations in both space and time remains a central challenge in complex systems, as measurements can be spatially incomp…
SENDAI: A Hierarchical Sparse-measurement, EfficieNt Data AssImilation Framework
Xingyue Zhang, Yuxuan Bao, Mars Liyao Gao +1
Bridging the gap between data-rich training regimes and observation-sparse deployment conditions remains a central challenge in spatiotemporal field reconstruction, particularly wh…
Cheap2Rich: A Multi-Fidelity Framework for Data Assimilation and System Identification of Multiscale Physics -- Rotating Detonation Engines
Yuxuan Bao, Jan Zajac, Megan Powers +2
Bridging the sim2real gap between computationally inexpensive models and complex physical systems remains a central challenge in machine learning applications to engineering proble…
Information theory and discriminative sampling for model discovery
Yuxuan Bao, J. Nathan Kutz
Fisher information and Shannon entropy are fundamental tools for understanding and analyzing dynamical systems from complementary perspectives. They can characterize unknown parame…
Data assimilation and discrepancy modeling with shallow recurrent decoders
Yuxuan Bao, J. Nathan Kutz
The requirements of modern sensing are rapidly evolving, driven by increasing demands for data efficiency, real-time processing, and deployment under limited sensing coverage. Comp…