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A physics-informed random feature method for countering part of the spectral bias which PINN-based solvers are facing for a certain class of PDEs, and develops a rigorous approximation error analysis and derive high-probability error bounds on the H^1$ norm.
This work introduces a general kernel-based encoder-decoder framework for operator learning that separates observation, representation, learning, and reconstruction, and develops this framework for multi-input, multi-output operator learning, where operators map between products of potentially distinct function spaces.
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