Leveraging Viscous Hamilton-Jacobi PDEs for Efficient Uncertainty Quantification in Scientific Machine Learning

Viscous Hamilton-Jacobi partial differential equations can be leveraged to efficiently solve Bayesian inference problems arising in scientific machine learning, enabling continuous model updates, hyperparameter tuning, and uncertainty quantification.