Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation

ArXi:2509.21711v2 Announce Type: replace-cross As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of research. Surrogate models that learn from data of multiple auxiliary modalities to the modeling of a highly expensive quantity of interest have the potential to aid outer loop applications such as optimization, inverse problems, or sensitivity analyses when multi-modal data are available.