用局部感知代理模型提升黑箱优化效率，尤其适合难求导的物理系统。
Locality-aware Surrogates for Gradient-based Black-box Optimization
- 基于梯度路径积分损失，让代理模型在局部保持梯度一致性。
- 在三个真实任务中，查询次数有限下优化效率显著提升。
- 适用于需可靠梯度估计的离线与在线优化场景。
在物理和工程领域，许多过程由不可微的黑箱模拟器建模，导致此类函数的优化尤为困难。受梯度定理启发，我们提出一种局部感知代理模型，用于主动的基于模型的黑箱优化。首先，我们建立了梯度对齐与梯度路径积分方程（GradPIE）损失之间的理论联系，该损失强制代理模型在设计空间的局部区域保持梯度一致性。基于此理论洞察，我们开发了一种可扩展的训练算法，以最小化GradPIE损失，实现离线与在线学习的同时保持计算高效。我们在三个真实任务上评估该方法：包括耦合非线性振子、模拟电路和光学系统等自动化体外实验，结果表明在有限查询预算下，优化效率持续提升。本研究为需要可靠梯度估计的离线与在线优化任务提供了稳健解决方案。
原文摘要 · Abstract (English)
In physics and engineering, many processes are modeled using non-differentiable black-box simulators, making the optimization of such functions particularly challenging. To address such cases, inspired by the Gradient Theorem, we propose locality-aware surrogate models for active model-based black-box optimization. We first establish a theoretical connection between gradient alignment and the minimization of a Gradient Path Integral Equation (GradPIE) loss, which enforces consistency of the surrogate's gradients in local regions of the design space. Leveraging this theoretical insight, we develop a scalable training algorithm that minimizes the GradPIE loss, enabling both offline and online learning while maintaining computational efficiency. We evaluate our approach on three real-world tasks - spanning automated in silico experiments such as coupled nonlinear oscillators, analog circuits, and optical systems - and demonstrate consistent improvements in optimization efficiency under limited query budgets. Our results offer dependable solutions for both offline and online optimization tasks where reliable gradient estimation is needed.
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