Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data

Understand

Active learning optimizes the exploration of large parameter spaces by strategically selecting which experiments or simulations to conduct, thus reducing resource consumption and potentially accelerating scientific discovery.

• A key component of this approach is a probabilistic surrogate model, typically a Gaussian Process (GP), which approximates an unknown functional relationship between control parameters and a target property.
• However, conventional GPs often struggle when applied to systems with discontinuities and non-stationarities, prompting the exploration of alternative models.
• This limitation becomes particularly relevant in physical science problems, which are often characterized by abrupt transitions between different system states and rapid changes in physical property behavior.