Presents structure-aware tensorial ROMs using Tucker factorization, general inner-product orthonormalization, and RBF interpolation for nonlinear parameter dependence and sparse data in PDEs.
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Embedding state constraints into Operator Inference yields reduced-order models that remain stable and physically consistent when extrapolating over 200% beyond the training regime for char combustion.
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Structure-Aware Tensorial Model Reduction
Presents structure-aware tensorial ROMs using Tucker factorization, general inner-product orthonormalization, and RBF interpolation for nonlinear parameter dependence and sparse data in PDEs.