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Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning.

, , , , , and . CoRR, (2018)

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Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning., , , , , and . CoRR, (2018)Time-series machine-learning error models for approximate solutions to parameterized dynamical systems., and . CoRR, (2019)Windowed least-squares model reduction for dynamical systems., and . CoRR, (2019)CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations., , , , , , , , and . ICLR, OpenReview.net, (2023)Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning., , , , , and . J. Comput. Phys., (2019)Projection-tree reduced order modeling for fast N-body computations., , , , , and . CoRR, (2021)Deep Conservation: A Latent-Dynamics Model for Exact Satisfaction of Physical Conservation Laws., and . AAAI, page 277-285. AAAI Press, (2021)Windowed least-squares model reduction for dynamical systems., and . J. Comput. Phys., (2021)Online adaptive basis refinement and compression for reduced-order models., and . CoRR, (2019)Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations., and . CoRR, (2018)