Profile
Prof. Antonio J. Gil graduated as Ingeniero de Caminos, Canales y Puertos from the University of Granada (Spain) in June 1999 (ranked 1st nationally) after having spent one academic year (1998-1999) in the University of California Davis fully funded by a prestigious California scholarship programme. After a two-year Certificate of Advanced Studies (MSc) in the field of Computational Mechanics, he moved to Swansea University where he completed his PhD in the field of computational analysis of nonlinear structural membranes in January 2005 (winner of the UK Association of Computational Mechanics best PhD paper 2004).
Having been awarded the National 1st Prize by the Spanish Ministry of Education in 2000, he has since received a number of further research prizes both as Principal Investigator and PhD supervisor, including the prestigious UK Philip Leverhulme Prize in 2011 and the ECCOMAS 2016 Olgierd Cecil Zienkiewicz award for his contributions as a young investigator in the field of computational mechanics.
Area of Expertise
- In-silico modelling
- Computational Mechanics
- Large strain dynamics
- Fluid-Structure Interaction
- Electro-Magneto-Acousto-Mechanics
- Finite Element/Volume Methods
- Meshless Methods
- Reduced Order Modelling
Projects
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DSLgene: Data-driven Statistical Learning of generalised mechanics of textile composites (funded by the Royal Society, Project number NIF\R1\241753)
Project Overview In this project, we aim to revolutionise the current artificial intelligence modelling approaches of textile reinforcements by integrating machine learning with the extensive wealth of physics knowledge. This facilitates the development of robust material models of textile reinforcements with unparalleled reliability (in terms of extrapolatability), thereby enabling the prediction of unseen scenarios with…
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In-silico Framework for the Design, Characterisation and Topology Optimisation of Electroactive Polymers for Soft Robotics
Project Overview This PhD project focuses on the development of advanced Machine Learning (ML) techniques to generate accurate and efficient constitutive models for complex smart materials – particularly Electroactive Polymers (EAPs). Traditional modelling approaches for such materials often rely on computationally intensive homogenisation schemes within Finite Element Methods (FEM), which can limit their practicality for…
Specialist Areas
Events
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Zienkiewicz Institute Community Event – 19th November 2025
Date: 19th November 2025 Time: 13:30 – 15:30 Location: Engineering North, 102 Speakers: Oubay Hassan, Siraj Shaikh Overview of Event: Dear colleague, For the upcoming ZI community event, Professor Oubay Hassan and Professor Siraj Shaikh have kindly agreed to share their recent experience in winning a Programme Grant and a Prosperity Partnership Grant. Date: Wednesday…
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Constitutive Modelling by Symbolic Regression
Time: 11:00 am – 12:00 pm, 9th September 2025 Location: Computational Foundry (Bay Campus), Robert Recorde Room (102) Speakers: Prof. Mikhail Itskov, Department of Continuum Mechanics, RWTH Aachen University, Aachen, Germany Overview of the Seminar: Symbolic regression represents an interesting method of machine learning which allows an unbiased, automated generation of constitutive models for various…