EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems - HetSys II
Lead Research Organisation:
University of Warwick
Department Name: Sch of Engineering
Abstract
Meeting emerging science and engineering modelling challenges requires scientists who can master complex theory and simulation techniques, can assimilate data, and can collaborate in multidisciplinary teams with expertise across a range of modelling scales. Securing the UK's position as a world-leading research hub into the future therefore requires a well-integrated pool of researchers with a skillset that is both broad and deep.
HetSys is leading the way in addressing these needs by producing students with the tools necessary to meet the challenges of the future through our training programme. We are training the scientists who will develop the next generation of computational models, implemented in reusable software with robust error bars from uncertainty quantification (UQ), and who can learn from experimental and simulated data on an equal footing through advances in 'scientific machine-learning' (SciML). Linking heterogeneous materials models with UQ allows performance to be improved, enabling the technology needed to reach net zero through a step-change in design capability. The ongoing AI revolution has necessitated a redesign of our training programme to enable us to build on what we learnt during the first funding period and deliver our new vision. In particular, changes to our core training enable our students to (i) embed robust and sustainable research software engineering (RSE) in modelling; (ii) quantify modelling uncertainties through enhanced use of statistical methods; and (iii) exploit new trends in scientific machine learning.
The research focus of HetSys on new paradigms in the behaviour of heterogeneous materials remains vital for the competitiveness of the UK's high-value manufacturing and automotive industries. Prominent examples of challenges we are addressing include the design of (i) energy materials for future vehicles with reduced carbon footprints; (ii) low dimensional and/or strongly correlated materials for quantum devices; (iii) high entropy alloys for fusion applications; (iv) biomolecules for combatting infectious diseases. Historically, the modelling pattern has focused on just one length- or time-scale; HetSys transforms this landscape by explicitly targeting the multiscale modelling of heterogeneous systems required by industry. The expertise we have accumulated opens up opportunities to capitalise on the transformative combination of mechanistic modelling with data-driven approaches (SciML). This requires a broader combination of disciplinary expertise, provided through our enhanced bespoke training programme.
Only a cohort approach can train high-quality computational scientists who can develop and implement new modelling methods in close collaboration with other scientists. The cohesive, interdepartmental cohorts and training programme we are creating lower many of the current barriers to interdisciplinary work and demonstrate our vision for the future of scientific endeavour, where teams of researchers work together to combine their skills and expertise. Only a critical mass of students and a large and highly collaborative team of supervisors makes this targeted and fully inclusive training approach feasible. HetSys supports the delivery of EPSRC's Physical and Mathematical Sciences Powerhouse strategic priority, helping to provide the platform on which research and innovation across the sciences is built.
HetSys is leading the way in addressing these needs by producing students with the tools necessary to meet the challenges of the future through our training programme. We are training the scientists who will develop the next generation of computational models, implemented in reusable software with robust error bars from uncertainty quantification (UQ), and who can learn from experimental and simulated data on an equal footing through advances in 'scientific machine-learning' (SciML). Linking heterogeneous materials models with UQ allows performance to be improved, enabling the technology needed to reach net zero through a step-change in design capability. The ongoing AI revolution has necessitated a redesign of our training programme to enable us to build on what we learnt during the first funding period and deliver our new vision. In particular, changes to our core training enable our students to (i) embed robust and sustainable research software engineering (RSE) in modelling; (ii) quantify modelling uncertainties through enhanced use of statistical methods; and (iii) exploit new trends in scientific machine learning.
The research focus of HetSys on new paradigms in the behaviour of heterogeneous materials remains vital for the competitiveness of the UK's high-value manufacturing and automotive industries. Prominent examples of challenges we are addressing include the design of (i) energy materials for future vehicles with reduced carbon footprints; (ii) low dimensional and/or strongly correlated materials for quantum devices; (iii) high entropy alloys for fusion applications; (iv) biomolecules for combatting infectious diseases. Historically, the modelling pattern has focused on just one length- or time-scale; HetSys transforms this landscape by explicitly targeting the multiscale modelling of heterogeneous systems required by industry. The expertise we have accumulated opens up opportunities to capitalise on the transformative combination of mechanistic modelling with data-driven approaches (SciML). This requires a broader combination of disciplinary expertise, provided through our enhanced bespoke training programme.
Only a cohort approach can train high-quality computational scientists who can develop and implement new modelling methods in close collaboration with other scientists. The cohesive, interdepartmental cohorts and training programme we are creating lower many of the current barriers to interdisciplinary work and demonstrate our vision for the future of scientific endeavour, where teams of researchers work together to combine their skills and expertise. Only a critical mass of students and a large and highly collaborative team of supervisors makes this targeted and fully inclusive training approach feasible. HetSys supports the delivery of EPSRC's Physical and Mathematical Sciences Powerhouse strategic priority, helping to provide the platform on which research and innovation across the sciences is built.
Organisations
- University of Warwick (Lead Research Organisation)
- Fraunhofer (Project Partner)
- Aix-Marseille University (Project Partner)
- Juelich Forschungszentrum (Project Partner)
- University of Stuttgart (Project Partner)
- Zenotech Ltd (Project Partner)
- Shanghai Jiao Tong University (Project Partner)
- The Falcon Project Ltd (Project Partner)
- Isaac Newton Institute (Project Partner)
- QinetiQ (Project Partner)
- Technical University Dresden (Project Partner)
- Nanjing University (Project Partner)
- University of Minnesota (Project Partner)
- JAGUAR LAND ROVER LIMITED (Project Partner)
- Innovate UK KTN (Project Partner)
- Waters Corporation (Project Partner)
- HIGH VALUE MANUFACTURING CATAPULT (Project Partner)
- Karlsruhe Institute of Technology (KIT) (Project Partner)
- Diamond Light Source (Project Partner)
- Ruhr University Bochum (Project Partner)
- Discover Materials (Project Partner)
- Dassault Systemes UK Ltd (Project Partner)
- Ansys UK Ltd (Project Partner)
- Pfizer Pharma GmbH (Project Partner)
- Fujitsu (Project Partner)
- Morgan Advanced Materials plc (UK) (Project Partner)
- Ca' Foscari University of Venice (Project Partner)
- University of Gothenburg (Project Partner)
- Henry Royce Institute (Project Partner)
- Free University of Brussels (VUB) (Project Partner)
- Adjacency Group (Project Partner)
- Oxford PV (Project Partner)
- Trinity College Dublin (Project Partner)
- University of British Columbia (Project Partner)
- TWI Ltd (Project Partner)
- Los Alamos National Laboratory (Project Partner)
- CCFE/UKAEA (Project Partner)
- Syngenta Ltd (Project Partner)
- Cresset BioMolecular Discovery Ltd (Project Partner)
- Johnson Matthey (Project Partner)
- The Faraday Institution (Project Partner)
- Beijing Normal University (Project Partner)
- ASTRAZENECA UK LIMITED (Project Partner)
- Atomic Weapons Establishment (Project Partner)
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/Y035429/1 | 30/09/2024 | 30/03/2033 | |||
| 2927348 | Studentship | EP/Y035429/1 | 30/09/2024 | 29/09/2028 | Gianluca Seaford |
| 2927305 | Studentship | EP/Y035429/1 | 30/09/2024 | 29/09/2028 | James Gulliford |
| 2929329 | Studentship | EP/Y035429/1 | 30/09/2024 | 29/09/2028 | Rumesh Sudhaharan |
| 2929344 | Studentship | EP/Y035429/1 | 30/09/2024 | 29/09/2028 | Keyi Wei |
| 2927358 | Studentship | EP/Y035429/1 | 30/09/2024 | 29/09/2028 | George Simmons |
| 2929345 | Studentship | EP/Y035429/1 | 30/09/2024 | 29/09/2028 | Swathi Mahashetti |
| 2927279 | Studentship | EP/Y035429/1 | 30/09/2024 | 29/09/2028 | Facundo Costa |
| 2927269 | Studentship | EP/Y035429/1 | 30/09/2024 | 29/09/2028 | David Bewicke |
| 2927298 | Studentship | EP/Y035429/1 | 30/09/2024 | 29/09/2029 | George McKay |
| 2929343 | Studentship | EP/Y035429/1 | 30/09/2024 | 29/09/2028 | Xiaopeng Wu |