  Fusion-graded plasmas are one of the physically complex systems, resulting in
continuous establishment of plasma theories for unclarified physical phenomena
in order to thoroughly control nuclear fusion reactors. Deep learning has drawn
vast attention to this field of controlled fusion plasma to link physical
phenomena with control-relevant parameters without a deepened understanding
about plasma theories. Albeit, quantifying the uncertainty of deep learning
models has been constantly requested due to their fundamental shortage of
physical understanding. Thus, a concept of a reliable deep learning model to be
able to present their probability distributions is raised as well as a method
to inculcate physical theories in the model is also concerned.
