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arXiv · 2501.19161

Locality-aware Surrogates for Gradient-based Black-box Optimization

Ali Momeni·Stefan Uhlich·Arun Venkitaraman·Chia-Yu Hsieh·Andrea Bonetti·Ryoga Matsuo·Eisaku Ohbuchi·Lorenzo Servadei
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Abstract

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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Ali Momeni, Stefan Uhlich, Arun Venkitaraman, Chia-Yu Hsieh, Andrea Bonetti, Ryoga Matsuo, Eisaku Ohbuchi, Lorenzo Servadei. 2025-01-31. Locality-aware Surrogates for Gradient-based Black-box Optimization. https://arxiv.org/abs/2501.19161

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