Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference

ArXi:2604.24911v1 Announce Type: new Machine Learning is becoming prevalent in science and engineering, but many approaches do not provide meaningful uncertainty estimates and predictions may also violate known physical knowledge. We propose a Bayesian framework to embed linear relationships across inputs and outputs into the learning process, whilst characterizing full predictive uncertainty over both the model parameters and the domain knowledge.