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Physics-informed sampler for solver-free data generation in operator learning
Neural operators offer fast surrogates for parametric PDEs but typically rely on costly datasets generated by repeated numerical solves. We propose a physics-informed sampler (PI-sampler) that generates PDE-consistent input--solution pairs without numerical PDE solves. The sampler draws candidate so
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