Energy-Conserved Neural Pipelines: Attenuating Error Propagation in Modular Neural Networks via Physical Conservation Constraints

In three linesResearchers propose enforcing energy conservation as a hard physical constraint in modular neural network pipelines to mitigate error propagation across module boundaries. On CIFAR-10, this approach retains 77.4% accuracy at sigma=0.2 versus 35.1% for baselines. The advantage generalizes to robotic systems (Franka Panda, MuJoCo) with +18.9 pp gain.

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