Strategic Package: Centre for Predictive Modelling in Science and Engineering
Lead Research Organisation:
University of Warwick
Department Name: Sch of Engineering
Abstract
Predictive modelling and uncertainty quantification are more than just another research direction relevant to science and engineering. They constitute a different way of thinking that impacts practically all aspects of scientific and engineering analysis and design. Rather than deriving deterministic answers to complex problems, distributions (error-bars) are obtained that account for our incomplete and often inaccurate information about the problems of interest.
Modelling of uncertain physical and geometric properties in the large scales, intrinsic randomness in the small scales or incorporation of stochastic closures necessitate the use of stochastic simulations in many multiscale problems, heavily taxing computational resources. The emerging exascale computers will allow stochastic simulations of realistic systems but analysing petabyte data bases with existing methods can be too inefficient, and full-scale models cannot be used in optimisation, design or real-time control. To this end, the University of Warwick proposes to establish a new centre focused on Predictive Modelling and uncertainty quantification with the mission to develop computational, mathematical and statistical methodologies for uncertainty quantification (UQ) and predictive modelling applied to a broad range of applications.
The new centre will emphasises a dynamic integration of several fundamental research areas including the following:
1) Formulation, analysis and numerical solution of stochastic multiscale/multiphysics systems exploring synergies between mathematical, statistical and machine learning approaches.
2) Data-driven development of certified stochastic reduced-order models.
3) Development of a probabilistic approach to systems synthesis, design, optimisation and control under uncertainty.
This proposed work will develop methods for quantifying uncertainty in multiscale simulations driven by experimental data collected at different scales. In order to account for the high-dimensional nature of the input uncertainties and resulting stochastic outputs, it is proposed to develop reduced-order surrogate models both for the input uncertainties and for the output of multiscale systems. Topics (1)-(3) are unifying themes that are common to many disciplines. Bringing together existent strengths within the University of Warwick to address these problems would allow simultaneously to impact multiple applications and move forward the problem of resolving bottlenecks in uncertainty quantification (e.g. high-dimensionality, limited data, long term time integration, rare events).
Modelling of uncertain physical and geometric properties in the large scales, intrinsic randomness in the small scales or incorporation of stochastic closures necessitate the use of stochastic simulations in many multiscale problems, heavily taxing computational resources. The emerging exascale computers will allow stochastic simulations of realistic systems but analysing petabyte data bases with existing methods can be too inefficient, and full-scale models cannot be used in optimisation, design or real-time control. To this end, the University of Warwick proposes to establish a new centre focused on Predictive Modelling and uncertainty quantification with the mission to develop computational, mathematical and statistical methodologies for uncertainty quantification (UQ) and predictive modelling applied to a broad range of applications.
The new centre will emphasises a dynamic integration of several fundamental research areas including the following:
1) Formulation, analysis and numerical solution of stochastic multiscale/multiphysics systems exploring synergies between mathematical, statistical and machine learning approaches.
2) Data-driven development of certified stochastic reduced-order models.
3) Development of a probabilistic approach to systems synthesis, design, optimisation and control under uncertainty.
This proposed work will develop methods for quantifying uncertainty in multiscale simulations driven by experimental data collected at different scales. In order to account for the high-dimensional nature of the input uncertainties and resulting stochastic outputs, it is proposed to develop reduced-order surrogate models both for the input uncertainties and for the output of multiscale systems. Topics (1)-(3) are unifying themes that are common to many disciplines. Bringing together existent strengths within the University of Warwick to address these problems would allow simultaneously to impact multiple applications and move forward the problem of resolving bottlenecks in uncertainty quantification (e.g. high-dimensionality, limited data, long term time integration, rare events).
Planned Impact
Predictive modelling is a key enabler for industry; it speeds up the design process, reduces the need for physical testing, reducse risk and enables rapid optimisation. The net effect is a significant reduction in cost and improved product development. The broad remit of the centre will ensure impact into a wide range of industrial sectors such as aerospace, energy, automotive and healthcare.
This research is of particular relevance and importance to the aerospace industry and most major corporations, e.g. General Electric, United Technologies, Boeing, Rolls Royce USA, Siemens USA, have significant activity in the area. We note that aerospace is a strategic priority for the UK government; in March 2013 the department of Business Innovation and Skills published its aerospace strategy, committing £1B to fund a research partnership with industry, with an additional £1B matched by industry. The importance of predictive modelling is now generally widely accepted by UK industry as highlighted by the recent TSB call 'Towards Zero prototyping" programme that is designed to promote the use of predictive modelling in engineering design and manufacturing and therefore reduce the need for physical prototypes. Additionally, 'advanced materials' is one of the UK government's 'eight great technologies' and an area where the UK has an international lead. UQ is already established in the US in the area of 'predictive materials science' and will increasingly impact on UK industry.
The centre will focus on the development of generic data-driven techniques and methodologies; impact will be delivered through tailored projects in collaboration with a wide range of industrial partners. The School of Engineering and WMG have extensive links with over 500 companies and hence rapid uptake of relevant methodologies can be facilitated. The centre will also proactively engage with industry to promote the use of predictive modelling techniques.
This research is of particular relevance and importance to the aerospace industry and most major corporations, e.g. General Electric, United Technologies, Boeing, Rolls Royce USA, Siemens USA, have significant activity in the area. We note that aerospace is a strategic priority for the UK government; in March 2013 the department of Business Innovation and Skills published its aerospace strategy, committing £1B to fund a research partnership with industry, with an additional £1B matched by industry. The importance of predictive modelling is now generally widely accepted by UK industry as highlighted by the recent TSB call 'Towards Zero prototyping" programme that is designed to promote the use of predictive modelling in engineering design and manufacturing and therefore reduce the need for physical prototypes. Additionally, 'advanced materials' is one of the UK government's 'eight great technologies' and an area where the UK has an international lead. UQ is already established in the US in the area of 'predictive materials science' and will increasingly impact on UK industry.
The centre will focus on the development of generic data-driven techniques and methodologies; impact will be delivered through tailored projects in collaboration with a wide range of industrial partners. The School of Engineering and WMG have extensive links with over 500 companies and hence rapid uptake of relevant methodologies can be facilitated. The centre will also proactively engage with industry to promote the use of predictive modelling techniques.
People |
ORCID iD |
Nigel Stocks (Principal Investigator) |
Publications
Aldegunde M
(2016)
Quantifying uncertainties in first-principles alloy thermodynamics using cluster expansions
in Journal of Computational Physics
Aldegunde M
(2016)
Development of an exchange-correlation functional with uncertainty quantification capabilities for density functional theory
in Journal of Computational Physics
Anand G
(2022)
Exploiting Machine Learning in Multiscale Modelling of Materials
in Journal of The Institution of Engineers (India): Series D
Barrera O
(2018)
Understanding and mitigating hydrogen embrittlement of steels: a review of experimental, modelling and design progress from atomistic to continuum.
in Journal of materials science
Bartók A
(2018)
Machine Learning a General-Purpose Interatomic Potential for Silicon
in Physical Review X
Bartók AP
(2017)
Machine learning unifies the modeling of materials and molecules.
in Science advances
Bianchini F
(2016)
Modelling defects in Ni-Al with EAM and DFT calculations
in Modelling and Simulation in Materials Science and Engineering
Bitzek E
(2015)
Atomistic aspects of fracture
in International Journal of Fracture
Brommer P
(2015)
Classical interaction potentials for diverse materials from ab initio data: a review of potfit
in Modelling and Simulation in Materials Science and Engineering
Caccin M
(2015)
A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers
in International Journal of Quantum Chemistry
Chatrabgoun O
(2018)
Approximating non-Gaussian Bayesian networks using minimum information vine model with applications in financial modelling
in Journal of Computational Science
Chen P
(2015)
Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference
in Journal of Computational Physics
Crevillén-García D
(2017)
Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media.
in Royal Society open science
Crevillén-García D
(2017)
Gaussian process modelling for uncertainty quantification in convectively-enhanced dissolution processes in porous media
in Advances in Water Resources
Daneshkhah A
(2017)
Probabilistic sensitivity analysis of optimised preventive maintenance strategies for deteriorating infrastructure assets
in Reliability Engineering & System Safety
Daneshkhah A
(2018)
Probabilistic Modeling of Financial Uncertainties
in International Journal of Organizational and Collective Intelligence
Darby J
(2022)
Compressing local atomic neighbourhood descriptors
in npj Computational Materials
Ellam L
(2016)
A Bayesian approach to multiscale inverse problems with on-the-fly scale determination
in Journal of Computational Physics
Esmaeilbeigi M
(2020)
A low cost and highly accurate technique for big data spatial-temporal interpolation
in Applied Numerical Mathematics
Ghiringhelli LM
(2023)
Shared metadata for data-centric materials science.
in Scientific data
Golebiowski JR
(2020)
Atomistic QM/MM simulations of the strength of covalent interfaces in carbon nanotube-polymer composites.
in Physical chemistry chemical physics : PCCP
Golebiowski JR
(2018)
Multiscale simulations of critical interfacial failure in carbon nanotube-polymer composites.
in The Journal of chemical physics
Goryaeva A
(2021)
Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W
in Physical Review Materials
Grigorev P
(2023)
Calculation of dislocation binding to helium-vacancy defects in tungsten using hybrid ab initio-machine learning methods
in Acta Materialia
Grigorev P
(2024)
matscipy: materials science at the atomic scale with Python
in Journal of Open Source Software
Grigorev P
(2020)
Hybrid quantum/classical study of hydrogen-decorated screw dislocations in tungsten: Ultrafast pipe diffusion, core reconstruction, and effects on glide mechanism
in Physical Review Materials
Hjorth Larsen A
(2017)
The atomic simulation environment-a Python library for working with atoms.
in Journal of physics. Condensed matter : an Institute of Physics journal
Katsoulakis M
(2017)
Special Issue: Predictive multiscale materials modeling
in Journal of Computational Physics
Kermode JR
(2015)
Low Speed Crack Propagation via Kink Formation and Advance on the Silicon (110) Cleavage Plane.
in Physical review letters
Khosrownejad S
(2021)
Quantitative prediction of the fracture toughness of amorphous carbon from atomic-scale simulations
in Physical Review Materials
Klawohn S
(2023)
Massively parallel fitting of Gaussian approximation potentials
in Machine Learning: Science and Technology
Klawohn S
(2023)
Gaussian approximation potentials: Theory, software implementation and application examples
in The Journal of Chemical Physics
Koutsourelakis P
(2016)
Special Issue: Big data and predictive computational modeling
in Journal of Computational Physics
Kristensen J
(2014)
Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method
in Computer Physics Communications
Kristensen J
(2015)
Predicting low-thermal-conductivity Si-Ge nanowires with a modified cluster expansion method
in Physical Review B
Lambert H
(2018)
Imeall: A computational framework for the calculation of the atomistic properties of grain boundaries
in Computer Physics Communications
Li Z
(2015)
Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces.
in Physical review letters
Onat B
(2020)
Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials.
in The Journal of chemical physics
Packwood D
(2016)
A universal preconditioner for simulating condensed phase materials.
in The Journal of chemical physics
Peguiron A
(2015)
Accuracy of buffered-force QM/MM simulations of silica.
in The Journal of chemical physics
Podgurschi V
(2022)
Atomistic modelling of iodine-oxygen interactions in strained sub-oxides of zirconium
in Journal of Nuclear Materials
Sernicola G
(2017)
In situ stable crack growth at the micron scale
in Nature Communications
Shah AA
(2017)
Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models.
in Proceedings. Mathematical, physical, and engineering sciences
Stephenson D
(2018)
Accelerating multiscale modelling of fluids with on-the-fly Gaussian process regression.
in Microfluidics and nanofluidics
Swinburne T
(2017)
Computing energy barriers for rare events from hybrid quantum/classical simulations through the virtual work principle
in Physical Review B
Tsilifis P
(2014)
Variational Reformulation of Bayesian Inverse Problems
Xing W
(2016)
Manifold learning for the emulation of spatial fields from computational models
in Journal of Computational Physics
Description | • Developed innovative techniques to quantify uncertainties in electronic structure calculations for computing material properties. This allows us to provide error bars on computed properties (e.g. bulk properties, band gaps, phase diagrams, etc.) induced by limited data to train surrogate models (cluster expansions). Such techniques are valuable in not only reliable property calculation but also for the design of new materials with desired properties. • Inverse problems were addressed for computing properties with multiresolution features (e.g. permeability or porosity fields in a reservoir engineering problem). The scale of estimation for such problems is computed directly from data. The developments include new mathematical models and algorithms using deep learning with wavelets but also potential new applications with technological significance (e.g. calculating properties of reservoirs using limited pressure or flow data, computing microstructures that result in desired properties, etc.) • We have extended the use of surrogate models in electronic structure calculations of bulk systems to open systems (surfaces and interfaces). This has allowed to compute properties of nanowires, as well to design nanostructures with desired properties (e.g. electrical conductivity). • We explored synergies between machine learning in the context of probabilistic graphical models and approximate inference algorithms to address uncertainty quantification of dynamical systems with multiple time scales. These techniques are important on predicting macroscopic behaviour from fine scale data as for example is the case in atomistic and molecular simulations of materials. • We are exploring the use of deep learning in the context of uncertainty quantification of multiscale systems using limited data. The challenges are many but there are potential rewards such as automatic recognition of the coarse grained variables and their distribution. • Organized two international meetings on Predictive Modelling (Predictive Multiscale Materials Modelling, Isaac Newton Institute, Cambridge, December 1-4, 2015 http://www.turing-gateway.cam.ac.uk/uqm_dec2015 and Big Data and Predictive Computational Modeling, IAS-TUM, Munich, May 18-20, 2015 http://www.tum-ias.de/bigdata2015/) |
Exploitation Route | Developed algorithms for computing material properties with error bars can be used to design new materials with unprecedented properties (e.g. nanowires with minimal electrical conductivity). Uncertainty quantification algorithms can be used as black box simulators for building data-driven predictive models in multiple applications using both experimental and simulation data. |
Sectors | Aerospace Defence and Marine Construction Digital/Communication/Information Technologies (including Software) Electronics Energy Healthcare Manufacturing including Industrial Biotechology |
URL | http://www2.warwick.ac.uk/wcpm/ |
Description | A number of study groups have taken place with industry. these have led to collaborations. Details can be found in the following links. https://warwick.ac.uk/fac/sci/wcpm/studygroup3/ https://warwick.ac.uk/fac/sci/wcpm/studygroup4/ https://warwick.ac.uk/fac/sci/hetsys/newsandevents/events/virtualstudygroup https://warwick.ac.uk/fac/sci/hetsys/newsandevents/events/industry2022/ |
First Year Of Impact | 2017 |
Description | (NOMAD CoE) - Novel Materials Discovery |
Amount | € 5,045,821 (EUR) |
Funding ID | 951786 |
Organisation | European Commission |
Sector | Public |
Country | European Union (EU) |
Start | 09/2020 |
End | 09/2023 |
Description | ARCHER eCSE |
Amount | £43,747 (GBP) |
Funding ID | eCSE11-7 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 08/2017 |
End | 02/2018 |
Description | EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems |
Amount | £5,752,474 (GBP) |
Funding ID | EP/S022848/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2019 |
End | 09/2027 |
Description | EPSRC Standard Mode |
Amount | £739,715 (GBP) |
Funding ID | EP/P022065/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2017 |
End | 03/2021 |
Description | EPSRC standard grant |
Amount | £442,958 (GBP) |
Funding ID | EP/R043612/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2018 |
End | 03/2022 |
Description | European Database for Multiscale Modelling of Radiation Damage (ENTENTE) |
Amount | € 4,000,000 (EUR) |
Funding ID | 900018 |
Organisation | European Commission |
Sector | Public |
Country | European Union (EU) |
Start | 08/2020 |
End | 08/2024 |
Description | Industrial CASE award |
Amount | £83,296 (GBP) |
Funding ID | 17000185 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2017 |
End | 09/2021 |
Description | Leverhulme Research Project Grant |
Amount | £385,476 (GBP) |
Organisation | The Leverhulme Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 03/2018 |
End | 03/2022 |
Description | Multiscale Simulation of Rarefied Gas Flow for Engineering Design |
Amount | £449,193 (GBP) |
Funding ID | EP/V012002/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 06/2021 |
End | 06/2024 |
Description | Royal Society Research Grant |
Amount | £14,764 (GBP) |
Funding ID | RG160691 |
Organisation | The Royal Society |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 03/2017 |
End | 03/2018 |
Description | Standard grant |
Amount | £823,000 (GBP) |
Funding ID | EP/R012474/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2018 |
End | 02/2021 |
Description | Support for advanced transition state search techniques in CASTEP |
Amount | £29,000 (GBP) |
Funding ID | 2nd ARCHER2 eCSE call |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2021 |
End | 08/2021 |
Description | UKIERI-DSF Partnership Development Workshop |
Amount | £15,000 (GBP) |
Organisation | British Council |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 01/2020 |
End | 12/2020 |
Description | Participation in Innovate UK/KTN Special Interest Group in Uncertainty Quantification and Management |
Organisation | Knowledge Transfer Network |
Country | United Kingdom |
Sector | Charity/Non Profit |
PI Contribution | Warwick Centre for Predictive Modelling is hosting an upcoming Industry Study Group on uncertainty quantification and management (UQ&M) for high value manufacturing in December 2017. Members of the Centre have also participated in other events organised by the UQ&M SIG, including: - JR Kermode, invited talk at KTN Uncertainty Quantification and Management for Industry, CFMS, Bristol, Aug 2017 - JR Kermode, Keynote at KTN event on Compound Semiconductor Simulation and Modelling, Birmingham, April 2017 - JR Kermode, Session chair at KTN event on Uncertainty Quantification and Management in High Value Manufacturing, London, June 2015 |
Collaborator Contribution | The KTN facilitated the events listed above, and is co-funding the WCPM-based Study Group to take place in December 2017. |
Impact | Multi-disciplinary: academic, government and industrial members. Industrial sectors include aerospace, automotive, infrastructure. |
Start Year | 2015 |