Process shift of multimode process involving data distribution and dynamic relation makes traditional transfer learning methods be intractable and even result in negative transfer. To tackle this ...

This article proposes a biobjective model-driven autoencoder network for blind HU that simultaneously addresses both linear and nonlinear relationships. By combining linear and nonlinear kernel models ...

We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...