Publications
Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleWarder SC, Piggott MD, 2022,
Optimal experiment design for a bottom friction parameter estimation problem
, GEM-INTERNATIONAL JOURNAL ON GEOMATHEMATICS, Vol: 13, ISSN: 1869-2672 -
Journal articleClare MCA, Wallwork JG, Kramer SC, et al., 2022,
Multi-scale hydro-morphodynamic modelling using mesh movement methods
, GEM: International Journal on Geomathematics, Vol: 13, ISSN: 1869-2672Hydro-morphodynamic modelling is an important tool that can be used in the protection of coastal zones. The models can be required to resolve spatial scales ranging from sub-metre to hundreds of kilometres and are computationally expensive. In this work, we apply mesh movement methods to a depth-averaged hydro-morphodynamic model for the first time, in order to tackle both these issues. Mesh movement methods are particularly well-suited to coastal problems as they allow the mesh to move in response to evolving flow and morphology structures. This new capability is demonstrated using test cases that exhibit complex evolving bathymetries and have moving wet-dry interfaces. In order to be able to simulate sediment transport in wet-dry domains, a new conservative discretisation approach has been developed as part of this work, as well as a sediment slide mechanism. For all test cases, we demonstrate how mesh movement methods can be used to reduce discretisation error and computational cost. We also show that the optimum parameter choices in the mesh movement monitor functions are fairly predictable based upon the physical characteristics of the test case, facilitating the use of mesh movement methods on further problems.
-
Journal articleMishra S, Flaxman S, Berah T, et al., 2022,
pi VAE: a stochastic process prior for Bayesian deep learning with MCMC
, STATISTICS AND COMPUTING, Vol: 32, ISSN: 0960-3174 -
Journal articleMishra S, Flaxman S, Berah T, et al., 2022,
π VAE: a stochastic process prior for Bayesian deep learning with MCMC
, Statistics and Computing, Vol: 32, ISSN: 0960-3174Stochastic processes provide a mathematically elegant way to model complex data. In theory, they provide flexible priors over function classes that can encode a wide range of interesting assumptions. However, in practice efficient inference by optimisation or marginalisation is difficult, a problem further exacerbated with big data and high dimensional input spaces. We propose a novel variational autoencoder (VAE) called the prior encoding variational autoencoder (πVAE). πVAE is a new continuous stochastic process. We use πVAE to learn low dimensional embeddings of function classes by combining a trainable feature mapping with generative model using a VAE. We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions such as their integrals. For popular tasks, such as spatial interpolation, πVAE achieves state-of-the-art performance both in terms of accuracy and computational efficiency. Perhaps most usefully, we demonstrate an elegant and scalable means of performing fully Bayesian inference for stochastic processes within probabilistic programming languages such as Stan.
-
Journal articleScoular J, Ghail R, Mason P, et al., 2022,
Are measured InSAR displacements a function of the chosen processing method?
, QUARTERLY JOURNAL OF ENGINEERING GEOLOGY AND HYDROGEOLOGY, Vol: 55, ISSN: 1470-9236 -
Journal articleFu Z, Ciais P, Feldman AF, et al., 2022,
Critical soil moisture thresholds of plant water stress in terrestrial ecosystems.
, Sci Adv, Vol: 8Plant water stress occurs at the point when soil moisture (SM) limits transpiration, defining a critical SM threshold (θcrit). Knowledge of the spatial distribution of θcrit is crucial for future projections of climate and water resources. Here, we use global eddy covariance observations to quantify θcrit and evaporative fraction (EF) regimes. Three canonical variables describe how EF is controlled by SM: the maximum EF (EFmax), θcrit, and slope (S) between EF and SM. We find systematic differences of these three variables across biomes. Variation in θcrit, S, and EFmax is mostly explained by soil texture, vapor pressure deficit, and precipitation, respectively, as well as vegetation structure. Dryland ecosystems tend to operate at low θcrit and show adaptation to water deficits. The negative relationship between θcrit and S indicates that dryland ecosystems minimize θcrit through mechanisms of sustained SM extraction and transport by xylem. Our results further suggest an optimal adaptation of local EF-SM response that maximizes growing-season evapotranspiration and photosynthesis.
-
Journal articleLittle A, Piggott MD, Buchan AG, 2022,
Authors reply to comment by Michio Aoyama on "Development of a gamma ray dose rate calculation and mapping tool for Lagrangian marine nuclear emergency response models" by Little et al.
, Mar Pollut Bull, Vol: 184 -
Journal articleGoren T, Feingold G, Gryspeerdt E, et al., 2022,
Projecting Stratocumulus Transitions on the Albedo-Cloud Fraction Relationship Reveals Linearity of Albedo to Droplet Concentrations
, GEOPHYSICAL RESEARCH LETTERS, Vol: 49, ISSN: 0094-8276 -
Journal articleJoshi J, Stocker BD, Hofhansl F, et al., 2022,
Towards a unified theory of plant photosynthesis and hydraulics
, NATURE PLANTS, ISSN: 2055-026X -
Journal articleLi Y, Tang Y, Toumi R, et al., 2022,
Revisiting the Definition of Rapid Intensification of Tropical Cyclones by Clustering the Initial Intensity and Inner-Core Size
, JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, Vol: 127, ISSN: 2169-897X
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.