Imperial’s c entre for computational methods in science and engineering

1. Imperial’s centre for computational methods in science and engineering Paul H J Kelly Co-Director Group Leader, Software Performance Optimisation Department of Computing, Imperial College London With DemetriosPapageorgiou (Dept of Mathematics) Pedro Baiz (Dept of Aeronautics) David Ham (Dept of Computing and Grantham Institute) http://www3.imperial.ac.uk/computationalmethods

2. Objectives • Provide a focus for development of foundational tools and methods that cut across individual disciplines • Facilitate cross-disciplinary research in computational methods and software tools (eg. to capitalize on current and future multicore and multithreaded hardware architectures) • Tackle the shortage of scientists with cross-disciplinary skills in computational methods and their application

3. This talk • Provide a focus for development of foundational tools and methods that cut across individual disciplines • Facilitate cross-disciplinary research in computational methods and software tools (eg. to capitalize on current and future multicore and multithreaded hardware architectures) • Tackle the shortage of scientists with cross-disciplinary skills in computational methods and their application • Illustrate the cross-disciplinary nature of computational methods research • Demonstrate the potential to bridge disciplines using software tools • Elaborate on what we aim to achieve in developing and communicating the skills we need

4. Example: finite element • Adaptive mesh finite-element CFD • FLUIDITY code • Imperial College Ocean Model (ICOM) • Higher-order finite-element methods • Nektar++ code • Applications in Formula 1, blood flow and electrical signalling in the heart • Extended finite-element methods (XFEM) • Using/extending the FEniCS package • Modelling cracks in composite structures • Monte-Carlo finite element methods • Extension to FLUIDITY • Applications to radiation transport in nuclear reactor safety • Tools for automatic generation of adjoint models • Applications to airframe optimisation, data assimilation, goal-directed meshing… • Illustrate the cross-disciplinary nature of computational methods research • Demonstrate the potential to bridge disciplines using software tools • Elaborate on what we aim to achieve in developing and communicating the skills we need

5. Example: finite element • FEniCS • A Python-based software framework for automated solution of differential equations by finite element methods • Common framework integrates many different methods • Generative: code generators embody sophisticated methods using powerful abstractions – without runtime overhead • FLUIDITY: • Unstructured mesh finite-element/control-volume CFD • Scaling to large-scale HPC systems • With dynamic mesh adaptivity • Multiphase flows • Nektar++: • tensor product based finite element package • High-order piecewise polynomial elements • Hybrid-shaped elements • Autotuning of operator implementations • Illustrate the cross-disciplinary nature of computational methods research • Demonstrate the potential to bridge disciplines using software tools • Elaborate on what we aim to achieve in developing and communicating the skills we need

7. beyond finite element • Illustrate the cross-disciplinary nature of computational methods research • Demonstrate the potential to bridge disciplines using software tools • Elaborate on what we aim to achieve in developing and communicating the skills we need • Particle-based models: • Molecular dynamics • Granular mechanics, DEM • Smoothed-particle hydrodynamics • Monte Carlo methods • Particle-in-cell • Coupled-cluster • Density functional theory • QM/MM • Bayesian inference • Markov chain Monte Carlo • And many many more…

8. Skills • Illustrate the cross-disciplinary nature of computational methods research • Demonstrate the potential to bridge disciplines using software tools • Elaborate on what we aim to achieve in developing and communicating the skills we need • What we want: • Computational scientists who understand how to • Deliver production-quality software • How to test it • How to generate robust, defensible, traceable computational results • Navigate the design space without prejudice • Computer scientists who know how to • Use the fundamental abstractions of numerical computing • Embody computational techniques in reusable tools • Get the abstraction right

9. Skills • Illustrate the cross-disciplinary nature of computational methods research • Demonstrate the potential to bridge disciplines using software tools • Elaborate on what we aim to achieve in developing and communicating the skills we need • What we’re doing: • Centre for Computational Methods • Based in Maths and Computing • Cutting across multiple disciplines • Undergraduate courses • Postgraduate courses • Integrated, structured doctoral programme • Focus for engagement with other international centres • Focus for engagement with key industry players

10. Centre for computational methods in science and engineering More than 60 computational methods specialists across the College Access to leading-edge HPC facilities and expertise Best practice software development for large-scale community codes Wide spectrum of industrial partners Graduating multidisciplinary computational scientists http://www3.imperial.ac.uk/computationalmethods