Self-supervised neural operator uses Bayesian PINNs to generate training data and a Transformer to learn PDE operators, achieving high accuracy on 1D/2D reaction-diffusion and fluid vibration problems with optional lightweight finetuning.
Cao, Choose a transformer: Fourier or galerkin, Advances in neural information processing systems 34 (2021) 24924–24940
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Self-supervised neural operator for solving partial differential equations
Self-supervised neural operator uses Bayesian PINNs to generate training data and a Transformer to learn PDE operators, achieving high accuracy on 1D/2D reaction-diffusion and fluid vibration problems with optional lightweight finetuning.