CompNO composes specialized Fourier neural operator blocks for fundamental differential operators into task-specific solvers that achieve lower L2 error than baselines on linear parametric PDEs and remain competitive on nonlinear flows while exactly satisfying boundaries.
Defining foun- dation models for computational science: A call for clarity and rigor
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Compositional Neural Operators decompose multi-dimensional fluid PDEs into a library of pretrained elementary physics blocks assembled via an aggregator that minimizes data and physics residuals.
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CompNO: A Novel Foundation Model approach for solving Partial Differential Equations
CompNO composes specialized Fourier neural operator blocks for fundamental differential operators into task-specific solvers that achieve lower L2 error than baselines on linear parametric PDEs and remain competitive on nonlinear flows while exactly satisfying boundaries.
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Compositional Neural Operators for Multi-Dimensional Fluid Dynamics
Compositional Neural Operators decompose multi-dimensional fluid PDEs into a library of pretrained elementary physics blocks assembled via an aggregator that minimizes data and physics residuals.