This is a project which is currently making use of HPC facilities at Newcastle University. It is active.
For further information about this project, please contact:
This project aims to develop probabilistic numerical methods for large-scale scientific computing, with a focus on sparse probabilistic Richardson extrapolation (SPRE). The goal is to accelerate high-fidelity numerical solvers and provide calibrated uncertainty quantification for complex physical simulation models. This work is geared towards large-scale differential equation solvers and multi-fidelity simulation workflows, aiming to build reliable and efficient computational processes for practical scientific applications.
We will use Python and scientific computing software including NumPy, SciPy, JAX, PyTorch and scikit-learn.