借力打力优化模型，提升不确定性下的预测稳定性。
AERO: A Redirection-Based Optimization Framework Inspired by Judo for Robust Probabilistic Forecasting
- 用柔道式引导思想重构优化过程，利用干扰而非对抗
- 在太阳能预测中显著提升准确率与适应性，噪声环境更优
- 适合高不确定性场景的模型优化，理论与应用兼备
优化仍是机器学习的核心，但在动态非线性系统中，尤其在不确定性环境下，现有方法常面临稳定性和适应性挑战。本文提出AERO（基于对抗能量的引导优化），受柔道‘借力打力’原理启发，将优化视为由15条相互关联公理指导的引导过程，涵盖对抗修正、能量守恒与扰动感知学习。通过梯度投影、不确定性驱动动态建模及学习能量管理，AERO提供了一种稳健且鲁棒的模型更新机制。应用于概率太阳能预测任务，AERO在噪声和不确定性环境中显著提升了预测精度、可靠性和适应能力。研究结果表明，AERO为优化的理论与实践提供了新方向。
原文摘要 · Abstract (English)
Optimization remains a fundamental pillar of machine learning, yet existing methods often struggle to maintain stability and adaptability in dynamic, non linear systems, especially under uncertainty. We introduce AERO (Adversarial Energy-based Redirection Optimization), a novel framework inspired by the redirection principle in Judo, where external disturbances are leveraged rather than resisted. AERO reimagines optimization as a redirection process guided by 15 interrelated axioms encompassing adversarial correction, energy conservation, and disturbance-aware learning. By projecting gradients, integrating uncertainty driven dynamics, and managing learning energy, AERO offers a principled approach to stable and robust model updates. Applied to probabilistic solar energy forecasting, AERO demonstrates substantial gains in predictive accuracy, reliability, and adaptability, especially in noisy and uncertain environments. Our findings highlight AERO as a compelling new direction in the theoretical and practical landscape of optimization.
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