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.
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Numerical study comparing feedforward NN and DeepONet with data-driven and physics-informed losses on stochastic heat equation, highlighting larger errors at distribution tails due to extrapolation.
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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.
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A numerical study into neural network surrogate model performance for uncertainty propagation
Numerical study comparing feedforward NN and DeepONet with data-driven and physics-informed losses on stochastic heat equation, highlighting larger errors at distribution tails due to extrapolation.