Publications (6)
Statistical Finite Elements via Langevin Dynamics
Ãmer Deniz Akyildiz, Connor Duffin, Sotirios Sabanis +1
The recent statistical finite element method (statFEM) provides a coherent statistical framework to synthesise finite element models with observed data. Through embedding uncertain…
-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation
Alex Glyn-Davies, Connor Duffin, Ã. Deniz Akyildiz +1
Incorporating unstructured data into physical models is a challenging problem that is emerging in data assimilation. Traditional approaches focus on well-defined observation operat…
Low-rank statistical finite elements for scalable model-data synthesis
Connor Duffin, Edward Cripps, Thomas Stemler +1
Statistical learning additions to physically derived mathematical models are gaining traction in the literature. A recent approach has been to augment the underlying physics of the…
Statistical Finite Elements via Interacting Particle Langevin Dynamics
Alex Glyn-Davies, Connor Duffin, Ieva Kazlauskaite +2
In this paper, we develop a class of interacting particle Langevin algorithms to solve inverse problems for partial differential equations (PDEs). In particular, we leverage the st…
Probabilistic Super-Resolution for High-Fidelity Physical System Simulations with Uncertainty Quantification
Pengyu Zhang, Connor Duffin, Alex Glyn-Davies +2
Super-resolution (SR) is a promising tool for generating high-fidelity simulations of physical systems from low-resolution data, enabling fast and accurate predictions in engineeri…
Exploring Model Misspecification in Statistical Finite Elements via Shallow Water Equations
Connor Duffin, Paul Branson, Matt Rayson +3
The abundance of observed data in recent years has increased the number of statistical augmentations to complex models across science and engineering. By augmentation we mean coher…