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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Kriging-based active learning efficiently maps rare instability regions in uncertain power systems and estimates their small probabilities, outperforming random forest active learning and non-active methods on IEEE 59-bus and WECC 240-bus test cases.
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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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Probabilistic Assessment of Rare Transient Instability Events via Kriging-based Active Learning Framework
Kriging-based active learning efficiently maps rare instability regions in uncertain power systems and estimates their small probabilities, outperforming random forest active learning and non-active methods on IEEE 59-bus and WECC 240-bus test cases.