Todd Oliver

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques

Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

Turbulence Modeling in the Age of Data

Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

Physics-informed neural networks for high-speed flows

High‐order CFD methods: current status and perspective

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

High-order methods for the Euler and Navier–Stokes equations on unstructured grids

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

Review of Output-Based Error Estimation and Mesh Adaptation in Computational Fluid Dynamics

Physics-informed machine learning

Uncovering turbulent plasma dynamics via deep learning from partial observations

A paradigm for data-driven predictive modeling using field inversion and machine learning

UNCERTAINTY QUANTIFICATION FOR RANS TURBULENCE MODEL PREDICTIONS

Bayesian uncertainty quantification applied to RANS turbulence models

Solving differential equations using deep neural networks

p-Multigrid solution of high-order discontinuous Galerkin discretizations of the compressible Navier–Stokes equations

Impact of Turbulence Model Irregularity on High-Order Discretizations

Bayesian uncertainty analysis with applications to turbulence modeling