Outputs (1774)

A reduced-order model for advection-dominated problems based on the Radon Cumulative Distribution Transform (2025)
Journal Article
Long, T., Barnett, R., Jefferson-Loveday, R., Stabile, G., & Icardi, M. (2025). A reduced-order model for advection-dominated problems based on the Radon Cumulative Distribution Transform. Advances in Computational Mathematics, 51(1), https://doi.org/10.1007/s10444-024-10209-5

Problems with dominant advection, discontinuities, travelling features, or shape variations are widespread in computational mechanics. However, classical linear model reduction and interpolation methods typically fail to reproduce even relatively sma... Read More about A reduced-order model for advection-dominated problems based on the Radon Cumulative Distribution Transform.

Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach (2025)
Journal Article
Evers, L. J., Raykov, Y. P., Heskes, T. M., Krijthe, J. H., Bloem, B. R., & Little, M. A. (2025). Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach. Sensors, 25(2), Article 366. https://doi.org/10.3390/s25020366

Objective and continuous monitoring of Parkinson’s disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detection of... Read More about Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach.

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.

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.

Touchdown-singularity formation and criticality in the thin-film equation (2024)
Journal Article
King, J. R., & Bowen, M. (in press). Touchdown-singularity formation and criticality in the thin-film equation. Philosophical Transactions A: Mathematical, Physical and Engineering Sciences,

The thin-film equation, ht = −(h n hxxx)x, is significant physically in the description of surface-tension-driven flows of thin films of viscous liquids and has served an important role mathematically in elucidating the properties and challenges of h... Read More about Touchdown-singularity formation and criticality in the thin-film equation.

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(1), 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.

An Introduction to p-adic L-functions (2024)
Journal Article
Rodrigues Jacinto, J., & Williams, C. (in press). An Introduction to p-adic L-functions. Essential Number Theory,

These expository notes introduce p-adic L-functions and the foundations of Iwasawa theory. We focus on Kubota Leopoldt's p-adic analogue of the Riemann zeta function, which we describe in three di erent ways. We rst present a measure-theoretic (anal... Read More about An Introduction to p-adic L-functions.

Probabilistic size-and-shape functional mixed models (2024)
Presentation / Conference Contribution
Wang, F., Bharath, K., Chkrebtii, O., & Kurtek, S. (2024, December). Probabilistic size-and-shape functional mixed models. Presented at Thirty-Eighth Annual Conference on Neural Information Processing Systems, Vancouver, Canada

The reliable recovery and uncertainty quantification of a fixed effect function µ in a functional mixed model, for modelling population-and object-level variability in noisily observed functional data, is a notoriously challenging task: variations al... Read More about Probabilistic size-and-shape functional mixed models.

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.