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We present Unified PDE Solvers (UPS), a data- and compute-efficient approach to developing unified neural operators for diverse families of spatiotemporal PDEs from various domains, dimensions, and resolutions.
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
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Deep equilibrium based neural operators for steady-state pdes
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