All Outputs (2)

Adjoint-aided inference of Gaussian process driven differential equations (2022)
Presentation / Conference Contribution
Gahungu, P., Lanyon, C. W., Álvarez, M. A., Smith, M. T., & Wilkinson, R. D. (2022). Adjoint-aided inference of Gaussian process driven differential equations. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems 35 (NeurIPS 2022)

Linear systems occur throughout engineering and the sciences, most notably as differential equations. In many cases the forcing function for the system is unknown, and interest lies in using noisy observations of the system to infer the forcing, as w... Read More about Adjoint-aided inference of Gaussian process driven differential equations.

Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds (2022)
Journal Article
Coveney, S., Roney, C. H., Corrado, C., Wilkinson, R. D., Oakley, J. E., Niederer, S. A., & Clayton, R. H. (2022). Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds. Scientific Reports, 12, Article 16572. https://doi.org/10.1038/s41598-022-20745-z

Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models fr... Read More about Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds.