TS002C AI and ML Techniques in Computational Mechanics III
Main Organizer:
Prof.
Elias Cueto
(
Universidad de Zaragoza
, Spain
)
Artificial intelligence and machine learning have burst onto the scene in our discipline, causing a revolution never before seen in its short history. Neural simulators and model reduction techniques, both linear and non-linear, scientific machine learning, etc., are now part of our everyday vocabulary. In this session, we aim to bring together the community working in this field and discuss the latest developments, along with the future that awaits us. We welcome contributions on topics such as (but not limited to) - Physics-informed ML - Discovery of constitutive laws by means of ML - Model Order Reduction - Neural simulators - Neural Operators - Generative AI - Reinforcement learning - ...
Scheduled presentations:
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Keynote
Scientific Machine Learning Enabled Digital Twin for Virtual Sensing in Aerospace Structural Health Monitoring
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A neural network machine-learning approach for characterising hydrogen trapping parameters from TDS experiments
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Graph Neural Networks for Arbitrary Auxetic Metamaterials
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Computer Vision-Augmented Digital Twins for Real-Time Sloshing via Structure-Preserving Deep Learning
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Coupling Data and Physical Information to Learn Hidden Structures with a Reliability-Aware Perspective