<ul><li>Diffusion models are effective for trajectory optimization but may violate critical constraints without explicit incorporation of constraint information.</li><li>A novel approach aligns diffusion models with problem-specific constraints using a hybrid loss function that measures and penalizes constraint violations during training.</li><li>The re-weighting strategy aligns predicted constraint violations to ground truth statistics, resulting in reduced violations compared to traditional diffusion models.</li><li>This approach can be integrated into the Dynamic Data-driven Application Systems (DDDAS) framework for efficient online trajectory adaptation.</li></ul>

Aligning Diffusion Model with Problem Constraints for Trajectory Optimization

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