  The widespread use of Deep Neural Networks (DNNs) has recently resulted in
their application to challenging scientific visualization tasks. While advanced
DNNs demonstrate impressive generalization abilities, understanding factors
like prediction quality, confidence, robustness, and uncertainty is crucial.
These insights aid application scientists in making informed decisions.
However, DNNs lack inherent mechanisms to measure prediction uncertainty,
prompting the creation of distinct frameworks for constructing robust
uncertainty-aware models tailored to various visualization tasks. In this work,
we develop uncertainty-aware implicit neural representations to model
steady-state vector fields effectively. We comprehensively evaluate the
efficacy of two principled deep uncertainty estimation techniques: (1) Deep
Ensemble and (2) Monte Carlo Dropout, aimed at enabling uncertainty-informed
visual analysis of features within steady vector field data. Our detailed
exploration using several vector data sets indicate that uncertainty-aware
models generate informative visualization results of vector field features.
Furthermore, incorporating prediction uncertainty improves the resilience and
interpretability of our DNN model, rendering it applicable for the analysis of
non-trivial vector field data sets.
