用强化学习自动发现伊辛模型的临界参数，无需人工干预。
Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning
- 设计物理启发的自适应强化学习框架，让智能体自主探索
- 精准同时识别临界温度与各类临界指数，抗扰动能力强
- 方法具可解释性，适合复杂物性系统自动化发现
传统方法确定临界参数常受人为因素影响。本研究提出一种物理启发的自适应强化学习框架，使智能体能自主与物理环境交互，精确同时识别伊辛模型的临界温度及多种临界指数。有趣的是，算法搜索行为类似相变过程，在不同初始条件下均能高效收敛至目标参数。实验表明，该方法在强扰动环境下显著优于传统方法。本研究不仅将物理概念融入机器学习以提升算法可解释性，更建立了一种从人工分析转向自主AI发现的新范式。
原文摘要 · Abstract (English)
Traditional methods for determining critical parameters are often influenced by human factors. This research introduces a physics-inspired adaptive reinforcement learning framework that enables agents to autonomously interact with physical environments, simultaneously identifying both the critical temperature and various types of critical exponents in the Ising model with precision. Interestingly, our algorithm exhibits search behavior reminiscent of phase transitions, efficiently converging to target parameters regardless of initial conditions. Experimental results demonstrate that this method significantly outperforms traditional approaches, particularly in environments with strong perturbations. This study not only incorporates physical concepts into machine learning to enhance algorithm interpretability but also establishes a new paradigm for scientific exploration, transitioning from manual analysis to autonomous AI discovery.
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