发表机构
Tsinghua University; Beijing Tsingyu Technology Co., Ltd.; The University of Hong Kong; Beijing Huairou Laboratory; Tanwei College, Tsinghua University; Shanxi Research Institute for Clean Energy, Tsinghua University（清华大学; 北京清钰科技有限公司; 香港大学; 北京怀柔实验室; 清华大学探微书院; 清华大学山西清洁能源研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对AI自主构建物理模型的局限，提出自进化AI科学家EvoSim，在工业电池建模任务中其预测精度超人类专家模型，自进化可降低约36%的模型与物理误差。
AI 中文摘要
基于物理的模型将科学解释与定量预测相连接，构建这类模型需要选择物理过程、定义状态与控制方程、指定耦合关系，并从实验中识别参数。现有AI系统在自主做出这些模型结构决策方面仍存在局限。我们提出EvoSim，一种用于物理建模的自进化AI科学家，它利用实验差异驱动机制与方程修订，并用预留的实验数据测试物理合理性；探索轨迹会更新知识、技能与多智能体协同策略，这种协同进化既改进了基于物理的模型，也提升了EvoSim选择机制、诊断故障与协调研究的能力。我们在两项工业电池建模任务上评估EvoSim：其一，它在25至45摄氏度、2C至6C的条件下预测锂金属 plating 起始点，其荷电状态的平均绝对误差为1.79%；其二，在车辆行驶工况下的动态电压预测任务中，它的均方根误差为7.62mV，超过了人类专家开发模型的报告精度。与基线相比，自进化将模型误差与物理误差降低了约36%，展现出更优的科学建模能力。EvoSim能将实验观测转化为经验证的模型与累积的研究专业知识。
英文摘要
Physics-based models connect scientific explanation with quantitative prediction. Constructing them requires selecting physical processes, defining states and governing equations, specifying couplings, and identifying parameters from experiments. Existing AI systems remain limited in making these model structure decisions autonomously. We introduce EvoSim, a self-evolving AI scientist for physical modeling. It uses experimental discrepancies to drive mechanism and equation revisions and held-out experimental data to test physical plausibility. Exploration traces make updates to knowledge, skills, and multi-agent orchestration. This co-evolution improves physics-based models and EvoSim's ability to select mechanisms, diagnose failures, and coordinate research. We evaluate EvoSim on two industrial battery modeling tasks. It predicts lithium-metal-plating onset from 25 to 45 degrees Celsius and 2 C to 6 C with a mean absolute error of 1.79% in state of charge. Dynamic voltage prediction under vehicle driving conditions achieves a root mean square error of 7.62 mV, surpassing the reported accuracy of models developed by human experts. Self-evolution reduces model and physics errors by approximately 36% relative to baseline, demonstrating improved scientific modeling capability. EvoSim turns experimental observations into validated models and cumulative research expertise.
Comments43 pages, 6 figures
