Adjoint A Posteriori Error Measures for Anisotropic Mesh Optimisation. - P. W. Power; Christopher C. Pain; M. D. Piggott; Fangxin Fang; Gerard J. Gorman; A. P. Umpleby; Anthony J. H. Goddard; I. M. Navon (Comput. Math. Appl., 2006)

Shoreline approximation for unstructured mesh generation. - Gerard J. Gorman; M. D. Piggott; Christopher C. Pain (Comput. Geosci., 2007)

A systematic approach to unstructured mesh generation for ocean modelling using GMT and Terreno. - Gerard J. Gorman; M. D. Piggott; M. R. Wells; Christopher C. Pain; Peter A. Allison (Comput. Geosci., 2008)

Simulating cardiac electrophysiology using anisotropic mesh adaptivity. - James Southern; Gerard J. Gorman; M. D. Piggott; Patrick E. Farrell; Miguel O. Bernabeu; Joe Pitt-Francis (J. Comput. Sci., 2010)

Anisotropic mesh adaptivity for cardiac electrophysiology. - James Southern; Gerard J. Gorman; M. D. Piggott; Patrick E. Farrell; Miguel O. Bernabeu; Joe Pitt-Francis (ICCS, 2010)

Parallel anisotropic mesh adaptivity with dynamic load balancing for cardiac electrophysiology. - James Southern; Gerard J. Gorman; M. D. Piggott; Patrick E. Farrell (J. Comput. Sci., 2012)

Hybrid OpenMP/MPI Anisotropic Mesh Smoothing. - Gerard J. Gorman; James Southern; Patrick E. Farrell; M. D. Piggott; Georgios Rokos; Paul H. J. Kelly (ICCS, 2012)

Tidal turbine array optimisation using the adjoint approach - Simon W. Funke; Patrick E. Farrell; M. D. Piggott (CoRR, 2013)

Hybrid global-local optimisation algorithms for the layout design of tidal turbine arrays. - George L. Barnett; Simon W. Funke; Matthew D. Piggott (CoRR, 2014)

PyRDM: A Python-based library for automating the management and online publication of scientific software and data. - Christian T. Jacobs; Alexandros Avdis; Gerard J. Gorman; Matthew D. Piggott (CoRR, 2014)

Integrating Research Data Management into Geographical Information Systems. - Christian T. Jacobs; Alexandros Avdis; Simon L. Mouradian; Matthew D. Piggott (SDA@TPDL, 2015)

Shoreline and Bathymetry Approximation in Mesh Generation for Tidal Renewable Simulations. - Alexandros Avdis; Christian T. Jacobs; Jon Hill; Matthew D. Piggott; Gerard J. Gorman (CoRR, 2015)

Design optimisation and resource assessment for tidal-stream renewable energy farms using a new continuous turbine approach. - Simon W. Funke; Stephan C. Kramer; Matthew D. Piggott (CoRR, 2015)

A correction to the enhanced bottom drag parameterisation of tidal turbines. - Stephan C. Kramer; Matthew D. Piggott (CoRR, 2015)

Anisotropic mesh adaptation in Firedrake with PETSc DMPlex. - Nicolas Barral; Matthew G. Knepley; Michael Lange 001; Matthew D. Piggott; Gerard J. Gorman (CoRR, 2016)

On the validity of tidal turbine array configurations obtained from steady-state adjoint optimisation. - Christian T. Jacobs; Matthew D. Piggott; Stephan C. Kramer; Simon W. Funke (CoRR, 2016)

Adaptive Haar wavelets for the angular discretisation of spectral wave models. - Alexandros Adam; Andrew G. Buchan; Matthew D. Piggott; Christopher C. Pain; Jon Hill; Mark A. Goffin (J. Comput. Phys., 2016)

Modelling local scour near structures with combined mesh movement and mesh optimisation. - J. M. Nunez Rattia; James R. Percival; S. J. Neethling; M. D. Piggott (J. Comput. Phys., 2018)

Unsupervised Learning of Particle Image Velocimetry. - Mingrui Zhang; Matthew D. Piggott (ISC Workshops, 2020)

Hydro-morphodynamics 2D modelling using a discontinuous Galerkin discretisation. - Mariana C. A. Clare; James R. Percival; Athanasios Angeloudis; Colin J. Cotter; Matthew D. Piggott (Comput. Geosci., 2021)

Learning to Estimate and Refine Fluid Motion with Physical Dynamics. - Mingrui Zhang; Jianhong Wang; James B. Tlhomole; Matthew D. Piggott (ICML, 2022)

M2N: Mesh Movement Networks for PDE Solvers. - Wenbin Song; Mingrui Zhang; Joseph G. Wallwork; Junpeng Gao; Zheng Tian 002; Fanglei Sun; Matthew D. Piggott; Junqing Chen; Zuoqiang Shi; Xiang Chen; Jun Wang 012 (NeurIPS, 2022)

Goal-Oriented Error Estimation and Mesh Adaptation for Tracer Transport Modelling. - Joseph G. Wallwork; Nicolas Barral; David A. Ham; Matthew D. Piggott (Comput. Aided Des., 2022)

Parallel Metric-Based Mesh Adaptation in PETSc using ParMmg. - Joseph G. Wallwork; Matthew G. Knepley; Nicolas Barral; Matthew D. Piggott (CoRR, 2022)

Bayesian neural networks for the probabilistic forecasting of wind direction and speed using ocean data. - Mariana C. A. Clare; Matthew D. Piggott (CoRR, 2022)

E2N: Error Estimation Networks for Goal-Oriented Mesh Adaptation. - Joseph G. Wallwork; Jingyi Lu; Mingrui Zhang; Matthew D. Piggott (CoRR, 2022)

End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning. - Siyi Li; Mingrui Zhang; Matthew D. Piggott (CoRR, 2022)

Calibration, inversion and sensitivity analysis for hydro-morphodynamic models through the application of adjoint methods. - Mariana C. A. Clare; Stephan C. Kramer; Colin J. Cotter; Matthew D. Piggott (Comput. Geosci., 2022)

Investigating microscale patchiness of motile microbes under turbulence in a simulated convective mixed layer. - Alexander Kier Christensen; Matthew D. Piggott; Erik van Sebille; Maarten van Reeuwijk; Samraat Pawar (PLoS Comput. Biol., 2022)

An unsupervised learning approach for predicting wind farm power and downstream wakes using weather patterns. - Mariana C. A. Clare; Simon C. Warder; Robert Neal; B. Bhaskaran; Matthew D. Piggott (CoRR, 2023)

Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models. - Sokratis Anagnostopoulos; Jens Bauer; Mariana C. A. Clare; Matthew D. Piggott (CoRR, 2023)

Learning to Optimise Wind Farms with Graph Transformers. - Siyi Li; Arnaud Robert; A. Aldo Faisal; Matthew D. Piggott (CoRR, 2023)

Neural Network as Transformation Function in Data Assimilation. - Kun Wang; Matthew D. Piggott; Yanghua Wang; Rossella Arcucci (ICCS (5), 2024)

Towards Universal Mesh Movement Networks. - Mingrui Zhang; Chunyang Wang; Stephan C. Kramer; Joseph G. Wallwork; Siyi Li; Jiancheng Liu; Xiang Chen; Matthew D. Piggott (NeurIPS, 2024)

Towards Universal Mesh Movement Networks. - Mingrui Zhang; Chunyang Wang; Stephan Kramer; Joseph G. Wallwork; Siyi Li; Jiancheng Liu; Xiang Chen; Matthew D. Piggott (CoRR, 2024)

Latent Three-Dimensional Variational Data Assimilation with Convolutional Autoencoder and LSTM for Flood Forecasting. - Kun Wang; Gabriele Bertoli; Kai Schröter; Enrica Caporali; Matthew D. Piggott; Yanghua Wang; Rossella Arcucci (ICCS (Workshops 4), 2025)

Machine learning for modelling unstructured grid data in computational physics: A review. - Sibo Cheng; Marc Bocquet; Weiping Ding 001; Tobias Sebastian Finn; Rui Fu; Jinlong Fu; Yike Guo; Eleda Johnson; Siyi Li; Che Liu; Eric Newton Moro; Jie Pan 007; et al. (Inf. Fusion, 2025)