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Generating Diverse Agricultural Data for Vision-Based Farming Applications
The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data.
Accurately Solving Rod Dynamics with Graph Learning
In this contribution, we introduce a novel method to accelerate iterative solvers for rod dynamics with graph networks (GNs) by predicting the initial guesses to reduce the number of iterations.
Accurately Solving Physical Systems with Graph Learning
In this contribution, we introduce a novel method to accelerate iterative solvers for physical systems with graph networks (GNs) by predicting the initial guesses to reduce the number of iterations.
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