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Experimental Design in the Age of GenAI  

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RCLW04 - Early Career Pioneers in Uncertainty Quantification and AI for Science

Generative AI stands to revolutionize how experiments are conceived, orchestrated, and iterated. This talk outlines a general framework that can harness generative AI within experimental design loops, then examines the widely adopted approach of Latent Space Bayesian Optimization. Our proposed methodology diverges from conventional frameworks by decoupling the discriminative (surrogate) and generative models, rather than tightly integrating Gaussian Processes with Variational Autoencoders. Through independent training and a straightforward Bayesian update, we enable an efficient sampling strategy that identifies candidate experiments with high scores.

This talk is part of the Isaac Newton Institute Seminar Series series.

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