Predictive Modelling Cluster
Predictive Modelling
Engineering the future with computational insightThe Predictive Modelling research cluster brings together a dynamic and interdisciplinary group of academic staff working across computational modelling, materials science, quantum technologies, nanotechnology and intelligent systems. Broadly, the cluster uses mathematical models and simulations to analyse complex systems, motivated by real-world problems across a broad range of research fields.
Statistical Foundations and Stochastic Processes
Focusses on statistical foundations, stochastic processes, Monte Carlo sampling and uncertainty quantification. Statistical foundations are the principles of probability theory that underpin statistical methods, while stochastic processes are mathematical models that describe systems evolving randomly over time. Their combination forms the basis for analysing and modelling unpredictable phenomena in areas spanning the natural sciences, statistical science, finance and beyond.
Quantum Device Modelling
The laboratory specializes in quantum engineering and modeling of advanced materials and devices, focusing on nanoscale electronic, vibrational, and magnetic properties, as well as quantum, phonon, and spin transport under various environmental effects. Research includes molecular electronics, energy harvesting, biological sensing, and 1D and 2D materials like graphene and carbon nanotubes. They utilize multiscale modeling methods, including density functional theory and quantum transport, co-developing the GOLLUM simulation tool for next-generation quantum transport studies.
Multiscale Materials Modelling
This laboratory specializes in computational and theoretical modeling of materials across scales, using multiscale methods, machine learning, and AI. Their work spans condensed matter physics, materials science, and computational chemistry, with strong collaborations and access to high-performance computing facilities.
Computational Nanotechnology
The laboratory focuses on theory and computational modeling of nanomaterials and devices, with expertise in electronic and phononic transport at the nanoscale, thermoelectric materials, nanotransistors, and electronic devices. Their work includes developing large-scale simulators and exploring energy conversion and generation technologies.
Connected Systems
Connectivity is the defining feature of the modern world, enabling many exciting technologies such as quantum machine learning, wireless communication, cybersecurity, smart grids, advanced robotics, networking, optical communication, optical sensing, as well as reconfigurable and hardware computing. From personal social interactions to crowd-driven applications, from device-to-device communication to long-distance remote sensing, and from vehicular networks to smart infrastructure, integrating computation with diverse communication methods is fundamental to this progress.
Cluster Leader: Prof. James Kermode
James is Professor of Materials Modelling in the School of Engineering at the University of Warwick. He also directs the EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (HetSys) and the Warwick Centre for Predictive Modelling (WCPM).
Cluster Convenor: Dr Michael Faulkner
Michael is an Assistant Professor in the Warwick Centre for Predictive Modelling. His academic career started as a PhD student at University College London and Ecole normale supérieure de Lyon from 2011 to 2015, under the co-supervision of Steve Bramwell and Peter Holdsworth. After a short postdoc and teaching position at Bristol Mathematics, he then moved to Bristol Physics in August 2017 after winning an EPSRC postdoctoral research fellowship. He was also a visiting scientist at Ecole normale supérieure (Paris) from September 2017 to October 2018, and won a Max Planck Institute research fellowship to visit the Max Planck Institute for the Physics of Complex Systems in Dresden in April 2018.
Key Publications
Anis, G., Hudson, T., Brommer, P., 2024. Dislocation dynamics in Ni: Parameterizing dislocation trajectories from atomistic simulations. Phys. Rev. Materials 8, 123604. https://doi.org/10.1103/PhysRevMaterials.8.123604
Chu, Z., Lakshminarayana, S., Chaudhuri, B., Teng, F., 2023. Mitigating Load-Altering Attacks Against Power Grids Using Cyber-Resilient Economic Dispatch. IEEE Trans. Smart Grid 14, 3164–3175. https://doi.org/10.1109/TSG.2022.3231563
Faulkner, M.F., 2024. Symmetry breaking at a topological phase transition. Phys. Rev. B 109, 085405. https://doi.org/10.1103/PhysRevB.109.085405
Faulkner, M.F., Livingstone, S., 2024. Sampling Algorithms in Statistical Physics: A Guide for Statistics and Machine Learning. Statist. Sci. 39. https://doi.org/10.1214/23-STS893
Goryaeva, A.M., Dérès, J., Lapointe, C., Grigorev, P., Swinburne, T.D., Kermode, J.R., Ventelon, L., Baima, J., Marinica, M.-C., 2021. Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W. Phys. Rev. Materials 5, 103803. https://doi.org/10.1103/PhysRevMaterials.5.103803
Graziosi, P., Li, Z., Neophytou, N., 2023. ElecTra code: Full-band electronic transport properties of materials. Computer Physics Communications 287, 108670. https://doi.org/10.1016/j.cpc.2023.108670
Jahangir, H., Lakshminarayana, S., Maple, C., Epiphaniou, G., 2023. A Deep-Learning-Based Solution for Securing the Power Grid Against Load Altering Threats by IoT-Enabled Devices. IEEE Internet Things J. 10, 10687–10697. https://doi.org/10.1109/JIOT.2023.3240289
Jing, J., Liu, K., Jiang, J., Xu, T., Wang, S., Liu, T., School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China, Key Laboratory of Opto-Electronics Information Technology, Ministry of Education, Tianjin University, Tianjin 300072, China, Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing, Tianjin University, Tianjin 300072, China, 2023. Highly sensitive and stable probe refractometer based on configurable plasmonic resonance with nano-modified fiber core. OEA 6, 220072–220072. https://doi.org/10.29026/oea.2023.220072
Jing, J., Liu, K., Jiang, J., Xu, T., Wang, S., Ma, J., Zhang, Z., Zhang, W., Liu, T., 2022. Performance improvement approaches for optical fiber SPR sensors and their sensing applications. Photon. Res. 10, 126. https://doi.org/10.1364/PRJ.439861
Khovanov, I.A., 2021a. Stochastic approach for assessing the predictability of chaotic time series using reservoir computing. Chaos: An Interdisciplinary Journal of Nonlinear Science 31, 083105. https://doi.org/10.1063/5.0058439
Khovanov, I.A., 2021b. The response of a bistable energy harvester to different excitations: the harvesting efficiency and links with stochastic and vibrational resonances. Phil. Trans. R. Soc. A. 379, rsta.2020.0245, 20200245. https://doi.org/10.1098/rsta.2020.0245
Klawohn, S., Darby, J.P., Kermode, J.R., Csányi, G., Caro, M.A., Bartók, A.P., 2023. Gaussian approximation potentials: Theory, software implementation and application examples. The Journal of Chemical Physics 159, 174108. https://doi.org/10.1063/5.0160898
Lakshminarayana, S., Kammoun, A., Debbah, M., Poor, H.V., 2021. Data-Driven False Data Injection Attacks Against Power Grids: A Random Matrix Approach. IEEE Trans. Smart Grid 12, 635–646. https://doi.org/10.1109/TSG.2020.3011391
Li, Q., Huang, H., Li, R., Lv, J., Yuan, Z., Ma, L., Han, Y., Jiang, Y., 2023. A comprehensive survey on DDoS defense systems: New trends and challenges. Computer Networks 233, 109895. https://doi.org/10.1016/j.comnet.2023.109895
Li, Z., Graziosi, P., Neophytou, N., 2022. Electron and Hole Mobility of SnO2 from Full-Band Electron–Phonon and Ionized Impurity Scattering Computations. Crystals 12, 1591. https://doi.org/10.3390/cryst12111591
Lin, Z., An, K., Niu, H., Hu, Y., Chatzinotas, S., Zheng, G., Wang, J., 2022a. SLNR-based Secure Energy Efficient Beamforming in Multibeam Satellite Systems. IEEE Trans. Aerosp. Electron. Syst. 1–4. https://doi.org/10.1109/TAES.2022.3190238
Lin, Z., Niu, H., An, K., Wang, Y., Zheng, G., Chatzinotas, S., Hu, Y., 2022b. Refracting RIS-Aided Hybrid Satellite-Terrestrial Relay Networks: Joint Beamforming Design and Optimization. IEEE Trans. Aerosp. Electron. Syst. 58, 3717–3724. https://doi.org/10.1109/TAES.2022.3155711
Rajkumar, A., Brommer, P., Figiel, Ł., 2023. An extensible density-biasing approach for molecular simulations of multicomponent block copolymers. Soft Matter 19, 1569–1585. https://doi.org/10.1039/D2SM01516A
Vincent, U.E., McClintock, P.V.E., Khovanov, I.A., Rajasekar, S., 2021. Vibrational and stochastic resonances in driven nonlinear systems: part 2. Phil. Trans. R. Soc. A. 379, 20210003. https://doi.org/10.1098/rsta.2021.0003
Wang, H., Li, Q., Sun, H., Chen, Z., Hao, Y., Peng, J., Yuan, Z., Fu, J., Jiang, Y., 2023. VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction. IEEE J. Select. Areas Commun. 41, 90–106. https://doi.org/10.1109/JSAC.2022.3221995
Wang, Z., Liu, Y., Gong, C., Yuan, Z., Shen, L., Chang, P., Liu, K., Xu, T., Jiang, J., Chen, Y.-C., Liu, T., 2021. Liquid crystal-amplified optofluidic biosensor for ultra-highly sensitive and stable protein assay. PhotoniX 2, 18. https://doi.org/10.1186/s43074-021-00041-1
Wardana, I.N.K., Fahmy, S.A., Gardner, J.W., 2023. TinyML Models for a Low-Cost Air Quality Monitoring Device. IEEE Sens. Lett. 7, 1–4. https://doi.org/10.1109/LSENS.2023.3315249
Wardana, I.N.K., Gardner, J.W., Fahmy, S.A., 2022. Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder. Neural Comput & Applic 34, 16129–16154. https://doi.org/10.1007/s00521-022-07224-2
Wardana, I.N.K., Gardner, J.W., Fahmy, S.A., 2021. Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction. Sensors 21, 1064. https://doi.org/10.3390/s21041064
Witt, W.C., Van Der Oord, C., Gelžinytė, E., Järvinen, T., Ross, A., Darby, J.P., Ho, C.H., Baldwin, W.J., Sachs, M., Kermode, J., Bernstein, N., Csányi, G., Ortner, C., 2023. ACEpotentials.jl: A Julia implementation of the atomic cluster expansion. The Journal of Chemical Physics 159, 164101. https://doi.org/10.1063/5.0158783
Yuan, Y., Zheng, G., Wong, K.-K., Letaief, K.B., 2021. Meta-Reinforcement Learning Based Resource Allocation for Dynamic V2X Communications. IEEE Trans. Veh. Technol. 70, 8964–8977. https://doi.org/10.1109/TVT.2021.3098854
Chu, Z., Lakshminarayana, S., Chaudhuri, B., Teng, F., 2023. Mitigating Load-Altering Attacks Against Power Grids Using Cyber-Resilient Economic Dispatch. IEEE Trans. Smart Grid 14, 3164–3175. https://doi.org/10.1109/TSG.2022.3231563
Faulkner, M.F., 2024. Symmetry breaking at a topological phase transition. Phys. Rev. B 109, 085405. https://doi.org/10.1103/PhysRevB.109.085405
Faulkner, M.F., Livingstone, S., 2024. Sampling Algorithms in Statistical Physics: A Guide for Statistics and Machine Learning. Statist. Sci. 39. https://doi.org/10.1214/23-STS893
Goryaeva, A.M., Dérès, J., Lapointe, C., Grigorev, P., Swinburne, T.D., Kermode, J.R., Ventelon, L., Baima, J., Marinica, M.-C., 2021. Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W. Phys. Rev. Materials 5, 103803. https://doi.org/10.1103/PhysRevMaterials.5.103803
Graziosi, P., Li, Z., Neophytou, N., 2023. ElecTra code: Full-band electronic transport properties of materials. Computer Physics Communications 287, 108670. https://doi.org/10.1016/j.cpc.2023.108670
Jahangir, H., Lakshminarayana, S., Maple, C., Epiphaniou, G., 2023. A Deep-Learning-Based Solution for Securing the Power Grid Against Load Altering Threats by IoT-Enabled Devices. IEEE Internet Things J. 10, 10687–10697. https://doi.org/10.1109/JIOT.2023.3240289
Jing, J., Liu, K., Jiang, J., Xu, T., Wang, S., Liu, T., School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China, Key Laboratory of Opto-Electronics Information Technology, Ministry of Education, Tianjin University, Tianjin 300072, China, Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing, Tianjin University, Tianjin 300072, China, 2023. Highly sensitive and stable probe refractometer based on configurable plasmonic resonance with nano-modified fiber core. OEA 6, 220072–220072. https://doi.org/10.29026/oea.2023.220072
Jing, J., Liu, K., Jiang, J., Xu, T., Wang, S., Ma, J., Zhang, Z., Zhang, W., Liu, T., 2022. Performance improvement approaches for optical fiber SPR sensors and their sensing applications. Photon. Res. 10, 126. https://doi.org/10.1364/PRJ.439861
Khovanov, I.A., 2021a. Stochastic approach for assessing the predictability of chaotic time series using reservoir computing. Chaos: An Interdisciplinary Journal of Nonlinear Science 31, 083105. https://doi.org/10.1063/5.0058439
Khovanov, I.A., 2021b. The response of a bistable energy harvester to different excitations: the harvesting efficiency and links with stochastic and vibrational resonances. Phil. Trans. R. Soc. A. 379, rsta.2020.0245, 20200245. https://doi.org/10.1098/rsta.2020.0245
Klawohn, S., Darby, J.P., Kermode, J.R., Csányi, G., Caro, M.A., Bartók, A.P., 2023. Gaussian approximation potentials: Theory, software implementation and application examples. The Journal of Chemical Physics 159, 174108. https://doi.org/10.1063/5.0160898
Lakshminarayana, S., Kammoun, A., Debbah, M., Poor, H.V., 2021. Data-Driven False Data Injection Attacks Against Power Grids: A Random Matrix Approach. IEEE Trans. Smart Grid 12, 635–646. https://doi.org/10.1109/TSG.2020.3011391
Li, Q., Huang, H., Li, R., Lv, J., Yuan, Z., Ma, L., Han, Y., Jiang, Y., 2023. A comprehensive survey on DDoS defense systems: New trends and challenges. Computer Networks 233, 109895. https://doi.org/10.1016/j.comnet.2023.109895
Li, Z., Graziosi, P., Neophytou, N., 2022. Electron and Hole Mobility of SnO2 from Full-Band Electron–Phonon and Ionized Impurity Scattering Computations. Crystals 12, 1591. https://doi.org/10.3390/cryst12111591
Lin, Z., An, K., Niu, H., Hu, Y., Chatzinotas, S., Zheng, G., Wang, J., 2022a. SLNR-based Secure Energy Efficient Beamforming in Multibeam Satellite Systems. IEEE Trans. Aerosp. Electron. Syst. 1–4. https://doi.org/10.1109/TAES.2022.3190238
Lin, Z., Niu, H., An, K., Wang, Y., Zheng, G., Chatzinotas, S., Hu, Y., 2022b. Refracting RIS-Aided Hybrid Satellite-Terrestrial Relay Networks: Joint Beamforming Design and Optimization. IEEE Trans. Aerosp. Electron. Syst. 58, 3717–3724. https://doi.org/10.1109/TAES.2022.3155711
Rajkumar, A., Brommer, P., Figiel, Ł., 2023. An extensible density-biasing approach for molecular simulations of multicomponent block copolymers. Soft Matter 19, 1569–1585. https://doi.org/10.1039/D2SM01516A
Vincent, U.E., McClintock, P.V.E., Khovanov, I.A., Rajasekar, S., 2021. Vibrational and stochastic resonances in driven nonlinear systems: part 2. Phil. Trans. R. Soc. A. 379, 20210003. https://doi.org/10.1098/rsta.2021.0003
Wang, H., Li, Q., Sun, H., Chen, Z., Hao, Y., Peng, J., Yuan, Z., Fu, J., Jiang, Y., 2023. VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction. IEEE J. Select. Areas Commun. 41, 90–106. https://doi.org/10.1109/JSAC.2022.3221995
Wang, Z., Liu, Y., Gong, C., Yuan, Z., Shen, L., Chang, P., Liu, K., Xu, T., Jiang, J., Chen, Y.-C., Liu, T., 2021. Liquid crystal-amplified optofluidic biosensor for ultra-highly sensitive and stable protein assay. PhotoniX 2, 18. https://doi.org/10.1186/s43074-021-00041-1
Wardana, I.N.K., Fahmy, S.A., Gardner, J.W., 2023. TinyML Models for a Low-Cost Air Quality Monitoring Device. IEEE Sens. Lett. 7, 1–4. https://doi.org/10.1109/LSENS.2023.3315249
Wardana, I.N.K., Gardner, J.W., Fahmy, S.A., 2022. Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder. Neural Comput & Applic 34, 16129–16154. https://doi.org/10.1007/s00521-022-07224-2
Wardana, I.N.K., Gardner, J.W., Fahmy, S.A., 2021. Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction. Sensors 21, 1064. https://doi.org/10.3390/s21041064
Witt, W.C., Van Der Oord, C., Gelžinytė, E., Järvinen, T., Ross, A., Darby, J.P., Ho, C.H., Baldwin, W.J., Sachs, M., Kermode, J., Bernstein, N., Csányi, G., Ortner, C., 2023. ACEpotentials.jl: A Julia implementation of the atomic cluster expansion. The Journal of Chemical Physics 159, 164101. https://doi.org/10.1063/5.0158783
Yuan, Y., Zheng, G., Wong, K.-K., Letaief, K.B., 2021. Meta-Reinforcement Learning Based Resource Allocation for Dynamic V2X Communications. IEEE Trans. Veh. Technol. 70, 8964–8977. https://doi.org/10.1109/TVT.2021.3098854
To be updated for the academic year 25/26
Undergraduate Courses
Learn how the interaction of hardware and software shapes our everyday lives and the future of industry.
Computer Systems Engineering is a fully integrated degree taught jointly with the Department of Computer Science.Link opens in a new window
Available Course Options
- BEng Computer Systems Engineering (G406)
- MEng Computer Systems Engineering (G408)
Postgraduate Taught Courses
Predictive Modelling and Scientific Computing (MSc/PGDip/PGCert/PGA)
Duration
- Full time:1 year (MSc)
- Part time:2 years (MSc)
Entry requirements: A minimum 2:1 undergraduate UK Honours degree or equivalent international qualification, in an engineering, physical sciences or mathematical subject.
Diagnostics, Data and Digital Health (MSc/PGDip/PGCert)
Duration:
- Full time:1 year (MSc)
- Part time:2 years (MSc)
Entry requirements: A minimum 2:1 undergraduate UK Honours degree or equivalent international qualification, in a physical sciences, engineering or another relevant subject.
Postgraduate Research Courses
Warwick’s School of Engineering has a vibrant postgraduate research community, with over 150 students pursuing postgraduate research degrees and more than 40 postdoctoral researchers.
Our position as a general engineering department strengthens our capabilities, enabling multi-disciplinary collaborative research. Our researchers engage with colleagues from across Warwick and beyond to develop innovative solutions to real-world challenges. Our partners include universities, SMEs, large businesses, NHS Trusts and charitable organisations.
Centres for Doctoral Training (CDTs)
Modelling of Heterogeneous Systems - HetSys
HetSys is an EPSRC-supported Centre for Doctoral Training. It recruits students from across physical sciences, mathematics and engineering who enjoy using their mathematical skills and thinking flexibly to solve complex problems. By developing these skills HetSys trains people to challenge current state-of-the-art in computational modelling of heterogeneous, ‘real world’ systems across a range of research themes such as nanoscale devices, new catalysts, superalloys, smart fluids, space plasmas etc.