<ul data-eligibleForWebStory="false"><li>Surrogate models are essential for emulating and quantifying uncertainty on expensive computer simulators for complex systems.</li><li>A new LOL-GP model is proposed, focusing on local transfer learning Gaussian Process for effective surrogate training on a target system using information from related source systems.</li><li>The LOL-GP incorporates a latent regularization model that identifies regions for beneficial transfer and areas where transfer should be avoided to mitigate the risk of negative transfer.</li><li>Numerical experiments and an application for jet turbine design demonstrate the improved surrogate performance of the LOL-GP over existing methods.</li></ul>

Local transfer learning Gaussian process modeling, with applications to surrogate modeling of expensive computer simulators

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