In this paper, we propose an approach for an application of Bayesian optimization using Sequential Monte Carlo (SMC) and concepts from the statistical physics of classical systems. Our method leverages the power of modern machine learning libraries such as NumPyro and JAX, allowing us to perform Bayesian optimization on multiple platforms, including CPUs, GPUs, TPUs, and in parallel. Our approach enables a low entry level for exploration of the methods while maintaining high performance. We present a promising direction for developing more efficient and effective techniques for a wide range of optimization problems in diverse fields.

翻译：本文提出一种结合序贯蒙特卡洛方法与经典系统统计物理概念的贝叶斯优化应用方案。该方法利用NumPyro、JAX等现代机器学习库的计算能力，可在CPU、GPU、TPU等多种平台并行执行贝叶斯优化。本方案在保持高性能的同时降低了方法探索的入门门槛，为跨领域优化问题开发更高效的技术提供了具有前景的研究方向。