Outputs (114)

A Shape-Newton method for free-boundary problems subject to the Bernoulli boundary condition (2024)
Journal Article
Fan, Y., Billingham, J., & van der Zee, K. (2024). A Shape-Newton method for free-boundary problems subject to the Bernoulli boundary condition. SIAM Journal on Scientific Computing, 46(6), A3599-A3627. https://doi.org/10.1137/23M1590263

We develop a shape-Newton method for solving generic free-boundary problems where one of the free-boundary conditions is governed by the nonlinear Bernoulli equation. The method is a Newton-like scheme that employs shape derivatives of the governing... Read More about A Shape-Newton method for free-boundary problems subject to the Bernoulli boundary condition.

Weak Sequential Completeness of Uniform Algebras (2024)
Journal Article
Feinstein, J. F., & Izzo, A. J. (2024). Weak Sequential Completeness of Uniform Algebras. Bulletin of the Irish Mathematical Society, 92(Winter), 27-32. https://doi.org/10.33232/bims.0092.27.32

We give a simple, elementary proof that a uniform algebra is weakly
sequentially complete if and only if it is finite-dimensional.

Laplace-based strategies for Bayesian optimal experimental design with nuisance uncertainty (2024)
Journal Article
Bartuska, A., Espath, L., & Tempone, R. (2025). Laplace-based strategies for Bayesian optimal experimental design with nuisance uncertainty. Statistics and Computing, 35, Article 12. https://doi.org/10.1007/s11222-024-10544-z

Finding the optimal design of experiments in the Bayesian setting typically requires estimation and optimization of the expected information gain functional. This functional consists of one outer and one inner integral, separated by the logarithm fun... Read More about Laplace-based strategies for Bayesian optimal experimental design with nuisance uncertainty.

Inverse Physics-Informed Neural Networks for transport models in porous materials (2024)
Journal Article
Berardi, M., Difonzo, F. V., & Icardi, M. (2025). Inverse Physics-Informed Neural Networks for transport models in porous materials. Computer Methods in Applied Mechanics and Engineering, 435, Article 117628. https://doi.org/10.1016/j.cma.2024.117628

Physics-Informed Neural Networks (PINN) are a machine learning tool that can be used to solve direct and inverse problems related to models described by Partial Differential Equations by including in the cost function to minimise during training the... Read More about Inverse Physics-Informed Neural Networks for transport models in porous materials.

Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses (2024)
Journal Article
Forrester, M., Petros, S., Cattell, O., Lai, Y. M., O’Dea, R. D., Sotiropoulos, S., & Coombes, S. (2024). Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses. PLoS Computational Biology, 20(12), Article e1012647. https://doi.org/10.1371/journal.pcbi.1012647

The ready availability of brain connectome data has both inspired and facilitated the modelling of whole brain activity using networks of phenomenological neural mass models that can incorporate both interaction strength and tract length between brai... Read More about Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses.

Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses (2024)
Journal Article
Forrester, M., Petros, S., Cattell, O., Lai, Y. M., ODea, R. D., Sotiropoulos, S., & Coombes, S. (2024). Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses. PLoS Computational Biology, 20(December), https://doi.org/10.1371/journal.pcbi.1012647

The ready availability of brain connectome data has both inspired and facilitated the modelling of whole brain activity using networks of phenomenological neural mass models that can incorporate both interaction strength and tract length between brai... Read More about Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses.

Monitoring university student response to social distancing policy during the SARS-CoV-2 pandemic using Bluetooth: the RADAR study (2024)
Journal Article
Bolton, K. J., Mendez-Villalon, A., Nanji, H., Jia, R., Ayling, K., Figueredo, G., & Vedhara, K. (2024). Monitoring university student response to social distancing policy during the SARS-CoV-2 pandemic using Bluetooth: the RADAR study. Mathematics in Medical and Life Sciences, 1(1), Article 2425096. https://doi.org/10.1080/29937574.2024.2425096

Aim: We use the Remote Assessment of Disease and Relapses platform (RADAR) to collect Bluetooth contact and location data from university students. We test the ability of this technology to objectively capture social interaction, explore the propensi... Read More about Monitoring university student response to social distancing policy during the SARS-CoV-2 pandemic using Bluetooth: the RADAR study.

Integrating human behaviour and epidemiological modelling: unlocking the remaining challenges (2024)
Journal Article
Hill, E. M., Ryan, M., Haw, D., Lynch, M. P., McCabe, R., Milne, A. E., Turner, M. S., Vedhara, K., Zeng, F., Barons, M. J., Nixon, E. J., Parnell, S., & Bolton, K. J. (2024). Integrating human behaviour and epidemiological modelling: unlocking the remaining challenges. Mathematics in Medical and Life Sciences, 1(1), Article 2429479. https://doi.org/10.1080/29937574.2024.2429479

This paper is part of a special issue on Behavioural Epidemiology.

Historically, responses to health-related emergencies (whether public health, veterinary health or plant health related) have exposed the deficiencies of mathematical models to inc... Read More about Integrating human behaviour and epidemiological modelling: unlocking the remaining challenges.

Systematics in tests of general relativity using LISA massive black hole binaries (2024)
Journal Article
Garg, M., Sberna, L., Speri, L., Duque, F., & Gair, J. (2024). Systematics in tests of general relativity using LISA massive black hole binaries. Monthly Notices of the Royal Astronomical Society, 535(4), 3283–3292. https://doi.org/10.1093/mnras/stae2605

Our current understanding is that an environment – mainly consisting of gas or stars – is required to bring massive black hole binaries (MBHBs) with total redshifted mass Mz ∼ [104, 107] M⊙ to the LISA band from parsec separation. Even in the gravita... Read More about Systematics in tests of general relativity using LISA massive black hole binaries.

5d 2-Chern-Simons Theory and 3d Integrable Field Theories (2024)
Journal Article
Schenkel, A., & Vicedo, B. (2024). 5d 2-Chern-Simons Theory and 3d Integrable Field Theories. Communications in Mathematical Physics, 405(12), Article 293. https://doi.org/10.1007/s00220-024-05170-9

The 4-dimensional semi-holomorphic Chern-Simons theory of Costello and Yamazaki provides a gauge-theoretic origin for the Lax connection of 2-dimensional integrable field theories. The purpose of this paper is to extend this framework to the setting... Read More about 5d 2-Chern-Simons Theory and 3d Integrable Field Theories.