Scalable multitask Gaussian processes for complex mechanical systems with functional covariates

Researchers introduced a scalable Gaussian process model for complex mechanical systems that can handle multiple tasks and functional covariates efficiently. This advancement allows for more accurate predictions and uncertainty quantification in digital simulations, requiring fewer data samples compared to traditional models, which is crucial for content creators working with time-dependent or spatially distributed data.

Read the full article at arXiv stat.ML

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