TS002E AI and ML Techniques in Computational Mechanics V
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
Bayesian Calibration of Steel Creep Model with ACBICI
-
Extension of GENERIC Informed Neural Network (GINN) to Transient Rheometry
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Learning the Behavior of Isotropic and Anisotropic Elastomers from a Mnimum Set of Experimental Data
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Bridging Physical Interpretability and Data-Driven Efficiency for History-Dependent ROM via SPILS-Net
-
A DeepONet Framework for Cardiac Electrophysiology Simulation