AeroJEPA applies joint-embedding predictive learning to produce scalable, semantically organized latent representations for 3D aerodynamic fields that support both field reconstruction and downstream design tasks.
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A pretrained GNN on 120M simulated LHC events improves downstream event-classification accuracy when training data are scarce, with benefits shrinking as data grow.
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AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling
AeroJEPA applies joint-embedding predictive learning to produce scalable, semantically organized latent representations for 3D aerodynamic fields that support both field reconstruction and downstream design tasks.
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Pretrained Event Classification Model for High Energy Physics Analysis
A pretrained GNN on 120M simulated LHC events improves downstream event-classification accuracy when training data are scarce, with benefits shrinking as data grow.