In daily life, we often encounter situations where it is difficult to obtain high-fidelity data during the training phase of some data predictions, such as changes in the earth’s atmosphere and heat conduction data. But for these experiments, we can use artificially simulated physical models to obtain a large amount of data. But these artificially simulated data often have a gap with the real situation, that is, low-fidelity data. How to train a model with a large number of low-fidelity data obtained by simulation combined with very few high-precision high-fidelity data so that the model has a high correct rate of prediction for high-fidelity data has become a problem worthy of research.
Tllokn/Deep-Multi-Fidelity-Gaussian-Process-Prediction
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