An Energy Transformer reconstructs full flow fields from patch-masked observations with only 10% of patches visible, achieving relative errors of 0.04 to 0.27 across three fluid mechanics datasets.
Sympnets: Intrinsic structure-preserving symplectic networks for identifying hamiltonian systems
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Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach
An Energy Transformer reconstructs full flow fields from patch-masked observations with only 10% of patches visible, achieving relative errors of 0.04 to 0.27 across three fluid mechanics datasets.