Neural operators approximate the solution operator for multi-task optimal control, generalizing to new tasks and enabling efficient adaptation via branch-trunk structure and meta-training.
Fine-tuning deeponets to enhance physics-informed neural networks for solving partial differential equations.arXiv preprint arXiv:2410.14134
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Neural Operators for Multi-Task Control and Adaptation
Neural operators approximate the solution operator for multi-task optimal control, generalizing to new tasks and enabling efficient adaptation via branch-trunk structure and meta-training.