Asena Karolin Özdemir

Sampling parallelism unlocks faster Bayesian learning by strategically distributing sample evaluations across GPUs, achieving near-perfect scaling and boosting augmentation diversity for quicker convergence.

Diffusion models can now outperform specialized architectures in the challenging task of inverse design for optical thin films, suggesting their broader applicability in materials science.