Dr Evan Ricketts

MMath PhD FHEA MIMMM

Publication

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Research

My research asks how we can model materials whose properties are uncertain and vary from place to place, and how those models can be made useful in practice. I work mainly on porous construction materials, soils and cementitious materials, using a combination of finite element methods, stochastic simulation and machine learning. The work has followed one path: representing material heterogeneity, simulating coupled processes through it, and now learning the physical models themselves from data.

Learning physical models from data

I develop neural methods that recover physical models rather than only predictions. Current work includes the automated discovery of water-retention laws for porous media using constrained network architectures, generative networks that learn the rules mapping latent random fields to microstructure, and physics-informed neural networks used both as surrogates and as finite element shape functions.

Stochastic microstructure and random fields

Building on my PhD, I work on Gaussian random field generators based on stochastic partial differential equations and their boundary treatment, plurigaussian simulation for generating multi-phase microstructures inside multi-level finite element frameworks, and exactly periodic stochastic representative volume elements for multiscale modelling. I am the author of DiffGRF, an open-source Python package that makes the whole random field generation process differentiable so that it can sit inside optimisation and learning workflows.

Coupled processes in heterogeneous porous media

I simulate unsaturated flow through spatially variable and water-repellent soils, reproducing the fingered and preferential flow seen in experiments, and have contributed to a probabilistic immersed (cut) finite element method with Bayesian model calibration for flow through rough cracks in concrete. Related work covers chloride transport in concrete and the triggering mechanics of microcapsules in self-healing cementitious materials.

Digital tools for material reuse

With support from the Institution of Civil Engineers and industry partners John F. Hunt and Arup, I am developing digital assessment tools for the rejuvenation and reuse of end-of-life construction materials. The first prototype, hosted online, optimises the particle size distribution of crushed waste concrete to reach a target permeability, showing how demolition material can be graded for reuse. Two PhD projects extend this work towards sustainable material reuse in civil infrastructure.

Projects and funding

• Institution of Civil Engineers Research and Development Fund: digital assessment of infrastructure materials for reuse (co-investigator)
• EPSRC Doctoral Landscape Award studentship: transforming civil infrastructure through sustainable material reuse (co-investigator)
• QAA Collaborative Enhancement Project, Geo-LLM: large language models in engineering education, with the Universities of Glasgow, Surrey and Manchester and industry partners John F. Hunt, Arup and CIRIA (principal investigator)
• GW4 Crucible seed-funded project (co-investigator)
• Royal Society International Exchange with the Singapore Institute of Technology

Collaborations

I work with colleagues at Bath University, Bristol University, Exeter University, Glasgow University, Manchester university, University of Surry, Ghent University, Politecnico di Milano and Politecnico di Bari, and across Cardiff Schools. Industry links with John F. Hunt, Arup, LUSAS and CIRIA span research, teaching and PhD supervision.

Teaching

I lead two modules in the School of Engineering.

Finite Element Theory and Practice (MEng and MSc)

Students build their own two-dimensional finite element code in MATLAB and apply commercial finite element software to problems in solid mechanics, with engineers from LUSAS supporting the computer laboratories. I have led the module since 2023.

Digital and Computational Architectural, Civil and Environmental Engineering (Year 3)

A core module I co-created in 2025 and lead, covering finite element analysis, computational and parametric design, data extraction from building information models, and data-driven and AI techniques in civil engineering. It is assessed entirely by portfolio, including oral and live-demonstration components.

I also supervise Year 3 and MSc dissertation projects, teach on the Year 2 field course, and act as a personal tutor.

Roles

• Departmental Assessment and Feedback Coordinator, Architectural, Civil and Environmental Engineering
• Member of the School of Engineering AI in Education Working Group
• Principal investigator of Geo-LLM, a QAA-funded project on large language models in engineering education
• Team member for the new BEng Civil Engineering and Construction programme delivered in Kazakhstan

Training and recognition

• Fellow of the Higher Education Academy, 2025
• Cardiff University Education Fellowship Programme, 2024
• Teaching of the Year award, School of Engineering, 2023–24

Biography

I joined Cardiff University as a Lecturer in Civil Engineering in October 2023, immediately after completing my PhD in the School of Engineering. My doctoral research developed stochastic finite element methods and random field generators for simulating unsaturated flow through heterogeneous and water-repellent soils.

Before that I studied Mathematics at Cardiff, graduating with an MMath (First Class Honours) in 2020 with a thesis on resonant triads of acoustic-gravity waves in shallow water.

I have taught in the School since 2020, first as a teaching assistant and now as module leader for finite element analysis at MEng and MSc level and for a core Year 3 module on digital and computational engineering that I co-created in 2025. I was awarded Fellowship of the Higher Education Academy in 2025 and the School’s Teaching of the Year award for 2023–24.

Honours and awards

• Member of the Institute of Materials, Minerals and Mining (MIMMM), 2026
• Selected participant, GW4 Crucible research leadership programme, 2026
• Fellow of the Higher Education Academy (FHEA), 2025
• Teaching of the Year award, School of Engineering, Cardiff University, 2023–24
• Travel grant, South Wales Institute of Engineers Educational Trust, 2023
• Cardiff University PhD Studentship, 2020

Professional memberships

• Institute of Materials, Minerals and Mining (Member, MIMMM), 2026 to present
• UK Association for Computational Mechanics (board member, Cardiff University representative), 2026 to present
• Advance HE (Fellow, FHEA), 2025 to present

Academic positions

• Lecturer in Civil Engineering (Teaching and Research), School of Engineering, Cardiff University, 2023 to present
• Teaching Assistant, School of Engineering, Cardiff University, 2020 to 2023

Speaking engagements

• UK Association for Computational Mechanics annual conference, University of Liverpool, April 2026
• Association for Civil Engineering Departments (ACED) annual conference, Leeds, September 2025: invited talk on local AI in assessment and simulation workflows
• UK Association for Computational Mechanics annual conference, Durham University, April 2024
• NUMGE 2023, 10th European Conference on Numerical Methods in Geotechnical Engineering, London, June 2023
• UNCECOMP 2023, 5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering, Athens, June 2023
• UK Association for Computational Mechanics annual conference, University of Warwick, April 2023
• ENOC 2022, 10th European Nonlinear Dynamics Conference, Lyon, July 2022
• UK Association for Computational Mechanics annual conference, University of Nottingham, April 2022

Committees and reviewing

• Departmental Assessment and Feedback Coordinator, Architectural, Civil and Environmental Engineering, Cardiff University, 2026 to present
• Board member, UK Association for Computational Mechanics, 2026 to present
• Internal reviewer for external funding applications, including Marie Skłodowska-Curie Actions fellowships and research council standard grants
• Reviewer for journals such as Computers & Structures, Engineering Applications of Artificial Intelligence and SoftwareX
• Examination boards (Year 3, Year 4, and MSc) and assessment moderation, School of Engineering

Supervisions

I welcome enquiries from prospective PhD students interested in computational mechanics and scientific machine learning applications to heterogeneous porous media. I am particularly keen to supervise projects addressing:

• Unfitted finite element methods for complex geometries
• Adjoint methods for optimisation problems
• Stochastic characterisation of material heterogeneity
• Stochastic methods for microstructural generation
• Physics-informed machine learning for porous media
• Sustainable geomaterials

Prospective students should have a strong background in computational mechanics, applied mathematics, or related engineering disciplines. Experience with finite element methods, scientific programming (Python/MATLAB), and machine learning frameworks would be advantageous.

Current supervision

Ahmed Chaudhry