Search Results - "Ham, David A"

Goal-Oriented Error Estimation and Mesh Adaptation for Tracer Transport Modelling by Wallwork, Joseph G., Barral, Nicolas, Ham, David A., Piggott, Matthew D.

“…This paper applies metric-based mesh adaptation methods to advection-dominated tracer transport modelling problems in two and three dimensions, using the…”
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Goal-oriented error estimation and mesh adaptation for shallow water modelling by Wallwork, Joseph G., Barral, Nicolas, Kramer, Stephan C., Ham, David A., Piggott, Matthew D.

“…This study presents a novel goal-oriented error estimate for the nonlinear shallow water equations solved using a mixed discontinuous/continuous Galerkin…”
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Automated shape differentiation in the Unified Form Language by Ham, David A., Mitchell, Lawrence, Paganini, Alberto, Wechsung, Florian

“…We discuss automating the calculation of weak shape derivatives in the Unified Form Language (ACM TOMS 40(2):9:1–9:37 2014 ) by introducing an appropriate…”
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Thetis coastal ocean model: discontinuous Galerkin discretization for the three-dimensional hydrostatic equations by Karna, Tuomas, Kramer, Stephan C, Mitchell, Lawrence, Ham, David A, Piggott, Matthew D, Baptista, Antonio M

“…Unstructured grid ocean models are advantageous for simulating the coastal ocean and river–estuary–plume systems. However, unstructured grid models tend to be…”
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Code Generation for Productive, Portable, and Scalable Finite Element Simulation in Firedrake by Betteridge, Jack D., Farrell, Patrick E., Ham, David A.

“…Creating scalable, high-performance PDE-based simulations requires an appropriate combination of models, discretizations, and solvers. The required combination…”
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Automatic adjoint-based inversion schemes for geodynamics: reconstructing the evolution of Earth's mantle in space and time by Ghelichkhan, Sia, Gibson, Angus, Davies, D. Rhodri, Kramer, Stephan C, Ham, David A

“…Reconstructing the thermo-chemical evolution of Earth's mantle and its diverse surface manifestations is a widely recognised grand challenge for the…”
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A study of vectorization for matrix-free finite element methods by Sun, Tianjiao, Mitchell, Lawrence, Kulkarni, Kaushik, Klöckner, Andreas, Ham, David A, Kelly, Paul HJ

“…Vectorization is increasingly important to achieve high performance on modern hardware with SIMD instructions. Assembly of matrices and vectors in the finite…”
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A streamline tracking algorithm for semi-Lagrangian advection schemes based on the analytic integration of the velocity field by Ham, David A., Pietrzak, Julie, Stelling, Guus S.

“…A new scheme for the construction on an unstructured grid of the streamlines of the three-dimensional shallow water equations is presented. The qualitative…”
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Slate: extending Firedrake's domain-specific abstraction to hybridized solvers for geoscience and beyond by Gibson, Thomas H, Mitchell, Lawrence, Ham, David A, Cotter, Colin J

“…Within the finite element community, discontinuous Galerkin (DG) and mixed finite element methods have become increasingly popular in simulating geophysical…”
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Algorithms for Mixed-Integer Optimization Constrained by Partial Differential Equations by Wesselhoeft, Christian, Ham, David A., Misener, Ruth

“…Mixed-integer, partial differential equation (PDE) constrained optimization (MIP-DECO) is a flexible modeling framework with many engineering applications,…”
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A structure-exploiting numbering algorithm for finite elements on extruded meshes, and its performance evaluation in Firedrake by Bercea, Gheorghe-Teodor, McRae, Andrew T. T, Ham, David A, Mitchell, Lawrence, Rathgeber, Florian, Nardi, Luigi, Luporini, Fabio, Kelly, Paul H. J

“…We present a generic algorithm for numbering and then efficiently iterating over the data values attached to an extruded mesh. An extruded mesh is formed by…”
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LBB stability of a mixed Galerkin finite element pair for fluid flow simulations by Cotter, Colin J., Ham, David A., Pain, Christopher C., Reich, Sebastian

“…We introduce a new mixed finite element for solving the 2- and 3-dimensional wave equations and equations of incompressible flow. The element, which we refer…”
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On the performance of a generic length scale turbulence model within an adaptive finite element ocean model by Hill, Jon, Piggott, M.D., Ham, David A., Popova, E.E., Srokosz, M.A.

“…► Coupling of a vertical turbulence parametrisation to an adaptive mesh ocean model. ► Adaptive mesh optimisation reduces computational load but maintains…”
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The symmetry and stability of unstructured mesh C-grid shallow water models under the influence of Coriolis by Ham, David A., Kramer, Stephan C., Stelling, Guus S., Pietrzak, Julie

“…The symmetry and stability properties of two unstructured C-grid discretisations of the shallow water equations are discussed. We establish that a scheme in…”
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Consistent point data assimilation in Firedrake and Icepack by Nixon-Hill, Reuben W., Shapero, Daniel, Cotter, Colin J., Ham, David A.

“…We present a high-level, differentiable, and composable abstraction for the point evaluation of the solution fields of partial differential equation models…”
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checkpoint_schedules: schedules for incremental checkpointing of adjoint simulations by Dolci, Daiane I., Maddison, James R., Ham, David A., Pallez, Guillaume, Herrmann, Julien

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A mixed discontinuous/continuous finite element pair for shallow-water ocean modelling by Cotter, Colin J., Ham, David A., Pain, Christopher C.

“…We introduce a mixed discontinuous/continuous finite element pair for ocean modelling, with continuous quadratic layer thickness and discontinuous velocity. We…”
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Editorial: The publication of geoscientific model developments v1.2 by Ham, David A., Hargreaves, Julia C., Kerkweg, Astrid, Roche, Didier M., Sander, Rolf

“…Version 1.1 of the editorial of Geoscientific Model Development (GMD), published in 2015 (GMD Executive Editors, 2015), introduced clarifications to the policy…”
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Physics-driven machine learning models coupling PyTorch and Firedrake by Bouziani, Nacime, Ham, David A

“…Partial differential equations (PDEs) are central to describing and modelling complex physical systems that arise in many disciplines across science and…”
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Differentiable programming across the PDE and Machine Learning barrier by Bouziani, Nacime, Ham, David A, Farsi, Ado

“…The combination of machine learning and physical laws has shown immense potential for solving scientific problems driven by partial differential equations…”
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