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Reliable prediction of turbulent flows is an important necessity acrossdifferent fields of science and engineering. In Computational Fluid Dynamics(CFD) simulations, the most common type of models are eddy viscosity modelsthat are computationally inexpensive but introduce a high degree of epistemicerror. The Eigenspace Perturbation Method (EPM) attempts to quantify thispredictive uncertainty via physics based perturbation in the spectralrepresentation of the predictions. While the EPM outlines how to perturb, itdoes not address how much or even where to perturb. We address this need byintroducing machine learning models to predict the details of the perturbation,thus creating a physics inspired machine learning (ML) based perturbationframework. In our choice of ML models, we focus on incorporating physics basedinductive biases while retaining computational economy. Specifically we use aConvolutional Neural Network (CNN) to learn the correction between turbulencemodel predictions and true results. This physics inspired machine learningbased perturbation approach is able to modulate the intensity and location ofperturbations and leads to improved estimates of turbulence model errors.

Multi-Fidelity Data Assimilation For Physics Inspired Machine Learning In Uncertainty Quantification Of Fluid Turbulence Simulations