CoSeC Conference 2023 - Presentations
Introduction
| Stephen Longshaw, UKRI-STFC: Welcome and Introduction |
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Computational Engineering
| Xiaoyuan Luo, University of Manchester: Digital Wave Flume Using Multi-Fidelity Approaches | Mark Pellowe, Loughborough University: An Adaptive Cost Function for a Multi-Fidelity Bayesian Optimisation Workflow |
Omar Mahfoze, UKRI-STFC: Multiscale Universal Interface for Hetrogeneous High-Performance Computing Systems |
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| [Download the slides]
| [Download the slides]
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Session 1 Questions
Scientific Reproducibility
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Jas Kalayan, UKRI-STFC: FAIR Data for the Biomolecular Simulation Community
| Joerg Sassmannshausen, Imperial College London: Scientific Reproducibility: The Importance of Reliable and Sustainable Software Installation
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Alin Elena, UKRI-STFC: Data to Knowledge
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[Download the slides]
| [Download the slides]
| [Download the slides]
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Session 2 Questions
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Materials Modelling
Harvey Devereux, Queen Mary University of London: Calculating System Properties on-the-fly in DL_POLY
| Maria Panoukidou, University of Edinburgh: Runaway Transitionin Irreversible Polymer Condensation with Cyclisation
| Michael Seaton, UKRI-STFC: Thermodynamically Consistent Multi-Phase DPD |
[Download the slides]
| [Download the slides]
| [Download the slides] |
Marcello Puligheddu, UKRI-STFC: Acceleration of Electronic Structure Codes on Heterogeneous Hardware
| Session 3 Questions
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Tomographic Imaging and Reproduction
Laura Murgatroyd, UKRI-STFC: Reproducible Tomographic Image Processing with CIL
| William Lionheart, University of Manchester: Computational Challenges in Rich Tomography - Creating an Integrated Computational Framework for Simultaneous CT Modelling and Reconstruction
| Session 4 Questions
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CoSeC Past / Present / Future
David Emerson, UKRI-STFC: From Concept to CoSeC: A CCP Tale
| Stephen Longshaw, UKRI-STFC: CoSeC: Now and the Future
| Session 5 Questions
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Community Feedback Session
CoSeC Conference 2022 - Presentations
Introduction
Stephen Longshaw, STFC: Introduction to CoSeC
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Data Science and Machine Learning
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Stefano Mensa, STFC Hartree Centre: Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
| Jiayun Pang, University of Greenwich: A Natural Language Processing (NLP)-Based Deep Learning Approach to Predict Solubility Parameters for Drug Discovery
| Yuhan Wang, University College London: Using molecular dynamics simulation to predict the aggregation propensity of mAb formulations & accelerate development
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Coupling and Data Workflows
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Wendi Liu, STFC: ParaSiF: A Partitioned Fluid-Structure Interaction Simulation Framework
| Raz Benson, University College London: Fast pattern detection in kinetic Monte Carlo simulations of heterogeneous catalysis
| Chien Nguyen, Bangor University: Reusing Previous Simulation Runs: Applications in Environmental
Fluid Dynamics
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High Performance and Future Computing
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Stefano Rolfo, STFC: Modernisation of the 2DECOMP&FFT Library for Hybrid Architecture
| Jianping Meng, STFC: Domain specific languages for exascale fluid simulations
| Jian Fang / Stefano Rolfo, STFC: Study of High-Speed Aerodynamics based on High-Fidelity Simulation
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CoSeC Conference 2021 - Presentations
(Unless otherwise stated, all presenters are employed by UKRI-STFC)
Introducing CoSeCExascale Computing
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Prof David Emerson: A million cores and beyond
| Dr Ian Bush: CRYSTAL22 - Thanks for the memory!
| Dr Phil Hasnip (University of York): Materials modelling and particle simulations at exascale: The PAX project
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Data Science
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Dr Agnel Praveen Joseph: Validating SARS-CoV2 cryo-EM structures and mapping genomic mutations
| Dr Edoardo Pasca: The CCPi Core Imaging Library. A versatile python library for tomographic imaging
| Dr Keith Butler: Simulation meets machine learning for supercharging science
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Scientific Challenges
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Dr Wei Wang: Nuclear thermal hydraulics for low-carbon applications - understanding energy and mass transport in advanced nuclear reactor systems
| Dr Ronan Keegan and Dr Adam Callison (University College London): Towards a quantum annealing algorithm for determining a crystal structure
| Dr Alin Elena: Machine learning for interatomic potentials
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