TS002A AI and ML Techniques in Computational Mechanics I
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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Recent advances in message-passing PDE solvers
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Variational Graph Neural Networks For Inverse Parameter Estimation
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Physics-Regularized State-Space Models for Space-Time Super-Resolution of Vortex-Dominated Flows.
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Increasing the efficiency of POD-like projection-based reduced-order models with plausible artificial snapshots