  This work presents a formalism to improve the predictive accuracy of physical
models by learning generalizable augmentations from sparse data. The proposed
approach, referred to as Learning and Inference assisted by Feature Engineering
(LIFE), is based on the hypothesis that robustness and generalizability demand
a meticulously-designed feature space that is informed by the underlying
physics, and a carefully constructed features-to-augmentation map. The critical
components of this approach are : (1)Identification of relevant
physics-informed features in appropriate functional forms to enable significant
overlap in feature space for a wide variety of cases to promote
generalizability; (2) Explicit control over feature space to locally infer the
augmentation without affecting other feature space regions, especially when
limited data is available; (3) Maintaining consistency across the learning and
prediction environments to make the augmentation case-agnostic; (4)
Tightly-coupled inference and learning by constraining the augmentation to be
learnable throughout the inference process to avoid significant loss of
information (and hence accuracy). To demonstrate the viability of this
approach, it is used in the modeling of bypass transition. The augmentation is
developed on skin friction data from two flat plate cases from the ERCOFTAC
dataset. The augmented model is then applied to a variety of flat plate cases
which are characterized by different freestream turbulence intensities,
pressure gradients, and Reynolds numbers. The predictive capability of the
augmented model is also tested on single-stage high-pressure-turbine cascade
cases, and the model performance is analyzed from the perspective of
information contained in the feature space. The results show consistent
improvements across these cases, as long as the physical phenomena in question
are well-represented in the training.
