Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference

Name: Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference
Uploaded: 2026-09-05T21:32:57.179703+00:00
Duration: 392 s
Description: The episode discusses 'Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference.' Hosts explain how this method uses active learning and prediction uncertainty to intelligently select optimal data points, resulting in more stable, accurate, and computationally efficient model reduction compared to random sampling.

The episode discusses 'Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference.' Hosts explain how this method uses active learning and prediction uncertainty to intelligently select optimal data points, resulting in more stable, accurate, and computationally efficient model reduction compared to random sampling.

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