CONFERENCE AGENDA

 

CONFERENCE AGENDA

Agenda

Key Information:

  • Day 1: 8:00 to 18:00,  Welcome Reception: 18:30
  • Day 2: 8:45 to 17:30, Gala Dinner: 19:00
  • Day 3: 9:00 to 14:00, NCC and Isambard AI Visit: 14:30

Download Full Programme *

* Preliminary Programme, might be subject to minor changes

AICOMP25 - Day 1 Schedule

AICOMP25 - Monday, 1st of September 2025

Monday, 1st of September 2025
08:00 Registration and coffee
09:00 Opening Ceremony
09:15 Plenary Lecture: Professor Adam Sobey
Programme Director for Data-Centric Engineering at The Alan Turing Institute, UK
Title TBC
Session 1 Session 2
AI-Driven Process Modelling and Simulation in Composites Mechanical Behaviour and Failure Prediction with AI
Time Type of presentation Authors Title Type of presentation Authors Title
10:10 Keynote Arghyanil Bhattacharjee¹, Kamyar Gordnian³, Reza Vaziri¹, Trevor C Campbell² and Anoush Poursartip¹³

¹ Composites Research Network, Departments of Materials Engineering and Civil Engineering, The University of British Columbia, Vancouver, BC, Canada

² Department of Statistics, The University of British Columbia, Vancouver, BC, Canada

³ Convergent Manufacturing Technologies, Vancouver, BC, Canada
AN UNCERTAINTY QUANTIFICATION FRAMEWORK FOR THERMAL MANAGEMENT IN COMPOSITES MANUFACTURING Keynote Wenbin Yu¹, Banghua Zhao¹ and R. Byron Pipes²

¹ School of Aeronautics and Astronautics, Purdue University, USA

² College of Engineering, Purdue University, USA
ADVANCING MULTISCALE MODELING OF COMPOSITES THROUGH ARTIFICIAL INTELLIGENCE
10:40 Oral Salman Zafar¹'², Mustafa Unel¹'² and Hatice S. Sas¹'²'³

¹ Faculty of Engineering and Natural Sciences, Sabanci University, Orhanli-Tuzla, Istanbul, Turkey

² Integrated Manufacturing Technologies Research and Application Center, Sabanci University, Orhanli-Tuzla, Istanbul, Turkey

³ School of Mechanical, Aerospace and Civil Engineering, The University of Sheffield, UK
DETECTING DEFECTS AND DISSIMILAR REGIONS IN LIQUID COMPOSITE MOLDING: A DEEP LEARNING BASED APPROACH TO RESIN FLOW MONITORING Oral Luca Patrignani, Silvestre T. Pinho

Department of Aeronautics, Imperial College London, London, UK
Graph Neural Networks for Efficient Prediction of Mechanical Response in Composite Structures with Models using Unstructured Meshes
11:00 Oral Jimmy G. Jean, Guillaume Broggi and Baris Caglar

Aerospace Structures and Materials Department, Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
IMAGE-BASED AI MODEL FOR PREDICTION OF MICROFLOW IN PROCESSING OF COMPOSITES Oral Jacintha Y.Y. Loh, Vincent B.C. Tan and Tong-Earn Tay

Department of Mechanical Engineering, National University of Singapore, Singapore
OPEN-HOLE TENSION STRENGTH PREDICTION WITH MACHINE LEARNING
11:20 Coffee Break
11:50 Oral S. Fernández-León¹'², D. Mocerino², J. Fernández-León¹, R. Valle¹, L. Baumela¹ and C. González²'³

¹ Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, E.T.S. de Ingenieros Informáticos, Madrid, Spain

² IMDEA Materiales, Madrid, Spain

³ Departamento de Ciencia de Materiales, Universidad Politécnica de Madrid, Madrid, Spain
Reinforcement Learning for Resilient Manufacturing of Structural Composites by Liquid Moulding Oral Kasper Foss Hansen, Dimitrios Bikos, and Soraia Pimenta

Department of Mechanical Engineering, Imperial College London, London, United Kingdom
CONVOLUTIONAL NEURAL NETWORKS FOR FAILURE PREDICTION IN TOW-BASED DISCONTINUOUS COMPOSITES
12:10 Oral Ashish Hegde, Dimitrios Zarouchas, Baris Caglar

Aerospace Structures and Materials Department, Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Graph Neural Network based surrogate for cure simulation of composites Oral S.Hasebe¹, R.Higuchi², T.Yokozeki² and S.Takeda

¹'² Department of Aeronautics and Astronautics, The University of Tokyo, Japan

³ Aviation Technology Directorate, Japan Aerospace Exploration Agency (JAXA), Tokyo, Japan
MACHINE LEARNING-BASED LOW-VELOCITY IMPACT DAMAGE PREDICTION FOR CARBON FIBER REINFORCED PLASTICS
12:30 Oral Siyuan Chen¹, Adam J. Thompson¹, Tim J. Dodwell²'³, Stephen R. Hallett¹ and Jonathan P.-H. Belnoue¹'⁴

¹ Bristol Composites Institute, University of Bristol, UK

² Department of Engineering, University of Exeter, UK

³ digiLab, The Innovation Centre, Exeter, UK

⁴ National Composites Centre, Bristol, UK
PROBABILISTIC AI FOR IMPROVED PROCESS ROBUSTNESS IN NON-CRIMP FABRIC FORMING Oral Luan Trinh¹, Quaiyum M. Ansari² and Paul Weaver³

¹ Faculty of Engineering and Technology, Technological University of the Shannon, Midlands Midwest, Ireland

² Department of Aerospace and Mechanical Engineering, South East Technological University, Carlow, Ireland

³ Bernal Institute, School of Engineering, University of Limerick, Ireland
MACHINE LEARNING-AIDED CLASSIFICATION OF BUCKLING BEHAVIOUR IN STIFFENED COMPOSITE CYLINDRICAL SHELLS WITH GEOMETRIC AND MATERIAL UNCERTAINTIES
12:50 Oral Julian Greif, Nils Meyer

Data-driven Product Engineering and Design, University of Augsburg, Germany
Fast prediction of warpage in injection-molded short fiber reinforced composites by coupling machine learning and differentiable FEM to incorporate real data Oral Christian Düreth¹, Andreas Hornig¹'², and Maik Gude¹

¹ Institute of Lightweight Engineering and Polymer Technology (TUD Dresden University of Technology, Germany)

² Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig (ScaDS.AI, TUD Dresden University of Technology, Germany)
Gaussian Process Regression for Multi-Modal Fatigue Crack Growth Identification in Textile-Reinforced Composites
13:10 Lunch
14:10 Plenary Lecture: Professor Francisco Chinesta

Professor of computational physics at Arts et Métiers Institute of Technology, Paris, France

Hybrid AI to enhance composites science, engineering and technology
15:00 Flash talks
D. Gray¹, Y. Chen¹, A. Rhead¹, R. Butler¹
¹ Department of Mechanical Engineering, University of Bath, UK
FOURIER NEURAL OPERATOR TO PREDICT MICROSCALE STRESS AND DAMAGE FIELDS IN COMPOSITES
Joseph Kirchhoff¹, Thomas O'Leary-Roseberry¹, John Yao¹, Dingcheng Luo¹, Yohannes Bekele¹, Tom Seidl², Tyler Hudson³, Andy Newman³, Wesley Tayon³, Mehran Tehrani⁴, Omar Ghattas¹
¹ The University of Texas at Austin, USA
² Sandia National Labs, USA
³ NASA Langley Research Center, USA
⁴ The University of California at San Diego, USA
MICRON-SCALE HETEROGENEOUS CHARACTERIZATION OF THERMOPLASTIC COMPOSITES VIA SEM-DIC, FINITE ELEMENTS & NEURAL OPERATORS
Kailun Deng¹, Hasan Caglar², Yifan Zhao¹ and David Ayre²
¹ Centre for Life-cycle Engineering and Management, Cranfield University, UK
² Composites and Advanced Materials Centre, Cranfield University, UK
Artificial Intelligence-Assisted Stacking Sequence Design in Composite Laminates
Kieran Guoite¹'², Chris Dighton¹, Cristian Lira¹, Ole T. Thomsen², Jonathan P.-H. Belnoue¹'²
¹ National Composites Centre, Feynman Way, Bristol, United Kingdom
² Bristol Composites Institute, University of Bristol, Queens Building, University Walk, Bristol, United Kingdom
Implementation of Gaussian Process Machine Learning for Resin Infusion Simulations
Yan Shen¹, Tianyou Yuan¹, Jun Zhou², Cheng Qiu³, Jinglei Yang¹'⁴
¹ Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology
² School of Nursing, Center of Smart Health, The Hong Kong Polytechnic University, HKSAR, China
³ Institute of Mechanics, Chinese Academy of Sciences, China
⁴ HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, China
Multimodal Fusion Learning for Sustainable Composite Manufacturing: Integrating Microstructural Imaging and Multi-Objective Process Optimization
Kuthan Çelebi¹, Oleksandr G. Kravchenko² and Sergii G. Kravchenko¹
¹ Department of Materials Engineering, The University of British Columbia
² Department of Mechanical and Aerospace Engineering, Old Dominion University, U.S.A
A DATA-DRIVEN SURROGATE MODELLING FRAMEWORK FOR MULTI-SCALE ANALYSIS OF MORPHOLOGICALLY COMPLEX COMPOSITES
Xin Lu¹, Ryo Higuchi¹ and Tomohiro Yokozeki¹
¹ Department of Aeronautics and Astronautics, The University of Tokyo, Japan
Open-source C++ user subroutines for scalable and data-driven fracture analysis of composite materials using Abaqus
Xiaohui Zhang¹, Ning Dong² and Gerhard Ziegmann³
¹ Institute for Polymer Materials and Plastics Technology, Clausthal University of Technology, Germany
² Institute for Informatics, Clausthal University of Technology, Germany
³ Institute for Polymer Materials and Plastics Technology, Clausthal University of Technology, Germany
Deep Learning-Based Flow Front Detection for In-Plane 1D Permeability Measurement
Dhiraj Biswas¹'⁴, Rajesh Nakka², Sathiskumar A. Ponnusami², Ganapathi A. Sengodan³
¹ Department of Materials, University of Oxford, United Kingdom
² Department of Engineering, City St George's, University of London, United Kingdom
³ School of Science, Engineering and Environment, University of Salford, United Kingdom
Microscale tensile-compressive response and fracture prediction of composites using multi-output CNN model
J. Seiffert¹, M. Ertl¹ and K. Drechsler¹
¹ Chair of Carbon Composites, Technical University of Munich, Germany
DEEP LEARNING TECHNIQUES FOR IN-SITU MISALIGNMENT QUANTIFICATION IN CONTINUOUS FIBER ADDITIVE MANUFACTURING
Mohammad N. Saquib¹, Richard Larson¹, Jiang Li², Sergii G. Kravchenko³ and Oleksandr G. Kravchenko¹
¹ Mechanical and Aerospace Engineering Department, Old Dominion University, USA
² Electrical and Computer Engineering Department, Old Dominion University, USA
³ Department of Materials Engineering, The University of British Columbia, Canada
RECONSTRUCTION OF FIBER ORIENTATION MORPHOLOGY IN MOLDED DISCONTINUOUS FIBER COMPOSITES USING RESIDUAL STRESS-BASED DEEP LEARNING
Ji Dong¹, Ali Kandemir², Ian Hamerton²
¹ School of Engineering Mathematics and Technology, University of Bristol, UK
² Bristol Composites Institute, School of Civil, Aerospace and Design Engineering, University of Bristol, UK
BEYOND PIXELS: NEURAL IMPLICIT REPRESENTATIONS FOR ACCURATE FIBRE ALIGNMENT IN COMPOSITES
15:40 Poster Session Followed by Coffee Break
Session 1 Session 2
AI-Driven Process Modelling and Simulation in Composites Mechanical Behaviour and Failure Prediction with AI
16:20 Oral Tim Newman¹, Cristian Lira¹, Jamie Hartley², Mindaugas Max Sasnauskas², Arjen Koorevaar³

¹ National Composites Centre, Engineering Development, Bristol, UK

² National Composites Centre, Manufacturing Development, Composite Moulding, Bristol, UK

³ Polyworx BV, Advanced Computing, Nijverdal, The Netherlands
Deep multi-agent reinforcement learning for vent and inlet positioning and quantity in resin transfer moulding Oral Chaeyoug Hong¹ and Wooseok Ji²

Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Republic of Korea
EFFICIENT TRAINING STRATEGY FOR A SCALABLE MICROMECHANICS MODEL PREDICTING LOCALIZED STRESSES BETWEEN FIBRES
16:40 Oral Liam D. Burns, Fadi El Kalach, Saeed Faharani, and Ramy Harik

Clemson Composites Center, Clemson University, Greenville, SC, USA
A Machine Learning Approach to Process Parameter Optimization of High-Pressure Resin Transfer Molding (HP-RTM) Systems Oral Mohammad N. Saquib¹, Richard Larson¹, Jiang Li², Sergii G. Kravchenko³ and Oleksandr G. Kravchenko¹

¹ Mechanical and Aerospace Engineering Department, Old Dominion University, USA

² Electrical and Computer Engineering Department, Old Dominion University, USA

³ Department of Materials Engineering, The University of British Columbia, Canada
FAILURE PREDICTION IN MOLDED COMPOSITES USING RESIDUAL STRESS AND DEEP LEARNING DRIVEN MICROSTRUCTURE RECONSTRUCTION
17:00 Oral Nuri Ersoy¹, Mehmet Can Engül, Pınar Acar²

¹ Boazici University, TURKEY

² Virginia Tech University, USA
A NEURAL NETWORK APPROACH TO COMPOSITES PROCESS SIMULATIONS Oral J. Gerritsen¹, A. Hornig¹'²'³ and M. Gude³

¹ Institute of Lightweight Engineering and Polymer Technology, TUD Dresden University of Technology, Germany

² Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig (ScaDS.AI), TUD Dresden University of Technology, Germany

³ Department of Engineering Science, Solid Mechanics and Materials Engineering, University of Oxford, OX1 3PJ, Oxford, United Kingdom
Influence of the sampling strategy for training data on edge case performance of data driven failure criterion for FRP
17:20 Oral Suplal Tudu and R. Velmurugan

Aerospace Engineering, Indian Institute of Technology Madras, India
Modelling of Curing Process of Fiber Reinforced Polymer Composites in Autoclave using AI Oral Runze Li, Mário Miranda and Silvestre T. Pinho

Department of Aeronautics, Imperial College London, London, UK
The uncertainty quantification of composite failure predictions based on Bayesian neural networks
17:40 Oral Oral Attada Phanendra Kumar¹, Shailesh Garg², Souvik Chakraborty², Dineshkumar Harursampath¹ and Sathiskumar Anusuya Ponnusami³

¹ Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, India

² Department of Applied Mechanics, Indian Institute of Technology Delhi, Delhi, India

³ Department of Engineering, City St George's, University of London, London, United Kingdom
Conformal prediction for uncertainty quantification of composite thermomechanical properties using randomised prior wavelet neural operators
18:00 End of the day 1
18:30 Welcome reception: Wills Memorial Hall
AICOMP25 - Day 2 Schedule

AICOMP25 - Tuesday, 2nd of September 2025

Tuesday, 2nd of September 2025
08:45 Registration and coffee
09:15 Plenary Lecture: Dr Pierre-Yves Mechin
Composites structural analyst & materials specialist, PlyNow
FROM MOLECULE TO STRUCTURE, COMPOSITES BEHAVIOUR AND PERFORMANCE ENHANCED BY AI
Session 3 Session 4
AI for Intelligent Composite Manufacturing and Control Data-Driven Surrogate Models and Accelerated Simulations
Time Type of presentation Authors Title Type of presentation Authors Title
10:10 Keynote Ramy Harik
Clemson Composites Center, Clemson University
Towards Smart Automated Fiber Placement Keynote Pavana Prabhakar¹, Haotian Feng², Sabarinathan P Subramaniyan³, and Hridyesh Tewani⁴

¹ Department of Mechanical Engineering, University of Wisconsin-Madison, USA

² Department of Civil & Environmental Engineering, University of Wisconsin-Madison, USA
AI FOR THE ANALYSIS, DESIGN, AND OPTIMIZATION OF COMPOSITES
10:40 Oral Anatoly Koptelov, Hanna Beketova, Jonathan P.-H. Belnoue, Stephen R. Hallett, Iryna Tretiak and Bassam El Said

Bristol Composites Institute, University of Bristol, Bristol, BS8 1TR, United Kingdom
Aspects of spatial time series forecasting for composite manufacturing problems of various dimensionality Oral M. Petrolo¹, M. Santori¹, K. Johnson², E. Zappino¹ and N. Zobeiry²

¹ MUL2 LAB, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy

² Department of Materials Science and Engineering, University of Washington, Seattle, WA, USA
ASSESSMENT OF MULTI-FIDELITY STRUCTURAL THEORIES TO TRAIN PROBABILISTIC MACHINE LEARNING FOR PROCESS-INDUCED DEFECTS
11:00 Oral Marcello Laurenti, Irene Bavasso, Erika Palazzi, Jacopo Tirillò, Fabrizio Sarasini and Filippo Berto

University of Rome "La Sapienza", Department of Chemical Engineering Materials Environment
AI-Driven Computational Framework for Optimizing FDM Process Parameters and Enhancing Mechanical Performance Oral S. Fernández-León¹'², D. Mocerino², R. Valle¹, L. Baumela¹ and C. González²'³

¹ Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Spain

² IMDEA Materiales, Madrid, Spain

³ Departamento de Ciencia de Materiales, Universidad Politécnica de Madrid, Spain
A deep surrogate model for accelerating LCM mould-filling simulations in unstructured and three-dimensional meshes
11:20 Coffee Break
11:50 Oral Jan Wolf¹, Aaron Vogel², Mathias Morgenstern², Christian Prescher³, Ulrich Burgbacher⁴, Manuel Prätorius⁴, Luis Garcia⁵, Benjamin Risse⁵, Thomas Behnisch¹ and Maik Gude¹

¹ Institute for Lightweight Engineering and Polymer Technology, TUD Dresden University of Technology, Germany

² SURAGUS GmbH, Germany

³ STRUCNAMICS Engineering GmbH, Germany

⁴ tapdo technologies GmbH, Germany
Tailoring carbon fibers combining novel inline sensors and machine learning Oral Aewis K.W. Hii, Stephen R. Hallett and Bassam El Said

Bristol Composites Institute, University of Bristol, UK
Modelling defects and progressive 3D failure in large composite components with shell elements: a data-driven, multi-scale approach
12:10 Oral Simone Bancora¹, Tim Newman¹, Paris Mulye²

¹ National Composites Centre, United Kingdom

² Quantiflex Simulations SAS, France
Use of Deep Reinforcement Learning to achieve Real-time Control of a Resin Infusion Process Oral Jens Wiegand¹ and Giuseppe Zumpano

¹ COMPACT Composite Impact Engineering LTD, UK

² Rolls Royce PLC, UK
Accelerating composite impact simulations by the use of neural network based surrogate models
12:30 Oral Patrick Flore, Fabian Röder, Kevin Chen, Andreas Gebhard

Leibniz-Institut für Verbundwerkstoffe, Germany
Geometry-Based Synthetic Data and Deep Learning for Online Surface Inspection of Fiber-Reinforced Composites Oral Shuang Yan¹, Mikhail Matveev¹, Michael Causon², Marco Iglesias², Andreas Endruweit¹, and Michael Tretyakov²

¹ Composites Research Group, Faculty of Engineering, University of Nottingham, Nottingham, UK

² School of Mathematical Sciences, Faculty of Science, University of Nottingham, Nottingham, UK
A SURROGATE MODEL FOR INVERSE PARAMETER ESTIMATION IN RESIN TRANSFER MOULDING PROCESSES
12:50 Lunch
Session 5 Session 6
AI in Non-Destructive Testing and Structural Health Monitoring AI-Driven Design and Industrial Applications in Composites
13:50 Oral A. Tabatabaeian¹, B. Jerkovic², P. Harrison¹, E. Marchiori² and M. Fotouhi³

¹ James Watt School of Engineering, University of Glasgow, UK

² Institute of Computing and Information Sciences, Radboud University, the Netherlands

³ Faculty of Civil Engineering and Geosciences, Delft University of Technology, the Netherlands
Detection of Barely Visible Impact Damage in CFRP Composite Panels Using Deep Learning Models and Hybrid Glass/Carbon Sensors Oral R.Zammit-Mangion¹, T.Ainsworth², C.Fallon¹, E.G.Loukaides¹ and F.Pinto¹

¹ Department of Mechanical Engineering, University of Bath, United Kingdom

² GKN Aerospace, Isle of White, United Kingdom
ENHANCING THE RELIABILITY OF ADHESIVELY BONDED AIRCRAFT JOINTS USING MACHINE LEARNING METHODS
14:10 Oral Fabing Emmanuel¹'², Méchin Pierre-Yves¹ and Keryvin Vincent²

¹ Dassault Systèmes, France

² Université Bretagne Sud, IRDL, France
AI BASED FIBRE MISALIGNMENT MEASUREMENT Oral Göran Fernlund¹'², Alireza Forghani¹, Anthony Floyd¹, and Anoush Poursartip¹'²

¹ Convergent Manufacturing Technologies, Canada

² The University of British Columbia, Materials Engineering, Canada
A structured approach to merging AI and ML into established composites process simulation and process analytics
14:30 Oral Jonas Naumann¹'², Jonas P. Appels¹, Philipp Sämann¹, Timo de Wolff² and Christoph Brauer¹

¹ Institute of Lightweight Systems, German Aerospace Center (DLR) Stade, Germany

² Institute of Analysis and Algebra, Technische Universität Braunschweig, Braunschweig, Germany
ENHANCING COMPOSITE MICROGRAPH ANALYSIS WITH SEMANTIC SEGMENTATION Oral J. Kucera

National Composites Centre, UK
CompoST – Composite Standard for composite data transfer and interoperability
14:50 Oral Mahoor Mehdikhani, Rui Guo, Shailee Upadhyay, Christian Breite, and Yentl Swolfs

Department of Materials Engineering, KU Leuven, Belgium
Segmentation in X-ray computed tomography images using deep learning Oral Hussain Abass¹'², Ross Allen¹, Andrew Corbett¹'³ and Ton Peijs²

¹ digiLab, UK

² WMG, University of Warwick

³ Alan Turing Institute, UK
FASTER ANALYSIS AND OPTIMISATION OF DISCONTINUOUS COMPOSITES USING MACHINE LEARNING
15:10 Coffee Break and Poster Session
15:50 Oral Umeir Khan¹, Vincent K. Maes¹, Rob Hughes², Petar Zivkovic³, Jon Wright³, Turlough McMahon³, and James Kratz¹

¹ Bristol Composites Institute, Department of Aerospace Engineering, University of Bristol, United Kingdom

² Department of Mechanical Engineering, University of Bristol, United Kingdom

³ Airbus UK, United Kingdom
Transfer Learning for Efficient Photo-Inspection of In-plane Waviness Oral J. Kucera, K. Angelov, P. Druiff

National Composites Centre, UK
National Composite Center upcoming projects
16:10 Oral Rui Guo, Mahoor Mehdikhani, Christian Breite and Yentl Swolfs

Materials Engineering, KU Leuven, Belgium
A 2D packing generator for unidirectional fibre-reinforced composites based on a Generative Adversarial Network Oral S. Psarras¹, G. Sotiriadis¹, M. Sergolle², T. Balutch², E. Billaudeau² and V. Kostopoulos¹

¹ Department of Mechanical Engineering & Aeronautics, University of Patras, Greece

² Naval Group, Centre d'Expertise des Structures et Matériaux Navals Technocampus Océan, France
ENHANCING REPAIR PATCH DESIGN FOR THICK COMPOSITE STRUCTURES IN NAVAL APPLICATION USING ARTIFICIAL INTELLIGENCE
16:30 Oral A. Gazzola, M. Quaresimin and M. Zappalorto

Department of Management and Engineering, University of Padova, Vicenza, Italy
DEEP LEARNING APPROACH FOR STRUCTURAL HEALTH MONITORING OF MULTIDIRECTIONAL LAMINATES VIA ELECTRICAL MEASUREMENTS Oral Yilun Dong¹, Zhong Zhang¹

¹ School of Engineering Science, University of Science and Technology of China, China
Artificial neural networks applied to polymer composites
16:50 Oral Mengyue He¹'², Zhifang Zhang¹ and Karthik Ram Ramakrishnan³

¹ Research Center for Wind Engineering and Engineering Vibration, Guangzhou University, Guangzhou, China

² College of Engineering, Shantou University, Shantou, China

³ Bristol Composites Institute, University of Bristol, United Kingdom
SUPPORT VECTOR MACHINE ALGORITHMS FOR DELAMINATION ASSESSMENT IN VIBRATION BASED SHM Oral Andrejs Krauklis¹

Latvia University of Life Sciences and Technologies, Faculty of Forest and Environmental Sciences, Institute of Civil Engineering and Woodworking, MEI Core Group, Latvia
MODULAR MATERIALS INFORMATICS FRAMEWORK FOR ASSESSING COMPOSITE MATERIAL AGEING
17:10 Oral Mihai M. Vasilache¹'², Iryna Tretiak³, Rostand B. Tayong¹ and Vladan Velisavljevic¹

¹ Institute for Research in Engineering and Sustainable Environment (IRESE), School of Computer Science and Technology, University of Bedfordshire, Luton, United Kingdom

² GKN Aerospace Service Limited, London Luton Airport, Luton, Bedfordshire, United Kingdom

³ Bristol Composites Institute, University of Bristol, United Kingdom
USING MACHINE LEARNING CONVOLUTION NEURAL NETWORK METHODS FOR THE ULTRASOUND CHARACTERISATION OF POROSITY ACROSS CARBON FIBRE REINFORCED POLYMER LAYERS Oral
17:30 End of the day 2
19:00 GALA DINNER: The Sansovino Hall at Bristol Harbour Hotel
AICOMP25 - Day 3 Schedule

AICOMP25 - Wednesday, 3rd of September 2025

Wednesday, 3rd of September 2025
09:00 Coffee and Pastries
09:30 Plenary Lecture: Dr Navid Zobeiry

Associate Professor in the Materials Science & Engineering Department at the University of Washington in Seattle, USA

Applied AI for Composites: From Accelerated Testing to Autonomous Certification and Smarter Engineering
Session 7
Physics-Informed Neural Networks (PINNs) and Physics-Based AI
Time Type of presentation Authors Title
10:20 Oral Ehsan Ghane¹, Marina Maia², Iuri Rocha², Martin Fagerström³, and Mohsen Mirkhalaf¹

¹ Department of Physics, University of Gothenburg, Sweden

² Department, Delft University of Technology, The Netherlands

³ Department of Industrial and Materials Science, Chalmers University of Technology, Sweden
HIERARCHICAL PHYSICALLY RECURRENT NEURAL NETWORKS FOR MULTI-SCALE MODELING OF WOVEN COMPOSITES
10:40 Oral Sahar Abouali, Anoush Poursartip, Reza Vaziri

Composites Research Network, Departments of Civil Engineering and Materials Engineering, The University of British Columbia, Vancouver, BC, Canada
A PHYSICS-INFORMED NEURAL NETWORK FOR INVERSE CHARACTERIZATION OF COMPOSITE DAMAGE MODELS USING FULL-FIELD EXPERIMENTAL DATA
11:00 Oral Bernabe Lorenzo Avila¹, Nils Meyer² and Dietmar Koch³

¹ Materials Engineering, University of Augsburg, Germany

² Data-driven Product Engineering and Design, University of Augsburg, Germany

³ Materials Engineering, University of Augsburg, Germany
Predicting Thermal Behaviour of C/C-SiC Fibre-Patched Structures Using Physics-Informed-Networks (PINNs)
11:20 Coffee break
11:50 Oral John M. Hanna¹'²

¹ Akhet solutions, Rennes, France

² Inria Rennes, France
Applications of Physics-informed neural networks to Liquid composite molding
12:10 Oral Tobias Würth¹, Niklas Freymuth², Gerhard Neumann² and Luise Kärger¹

¹ Institute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), Germany

² Autonomous Learning Robots, Karlsruhe Institute of Technology (KIT), Germany
Physics-Informed MeshGraphNets (PI-MGNs) for ansiotropic materials
12:30 Oral Shady Adib¹, Ieva Misiunaite²

¹ School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom

² Research Laboratory of Innovative Building Structures, Department of Aeronautical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania
AI-Enhanced Topology Optimisation of PLA Lattice Structures for Hybrid Composite Applications: Exploring Physics-Informed Approaches
12:50 Closing Ceremony
13:00 Farewell Lunch
14:00 End of conference.

Optional tour to the National Composite Centre and Isambard AI

Start at 14:30 , estimated time return time 17:00 (TBC)