基于人工智能的因果模型推断物理定律
Inferring physical laws by artificial intelligence based causal models
AI总结：
针对现有机器学习多为关联分析、缺乏因果推理的问题，提出结合因果推断与干预的物理原理因果学习模型，可识别变量因果关系，辅助验证物理过程模型。
摘要：
人工智能（AI）与机器学习（ML）的进步为科学研究开辟了诸多途径，也正在为知识创造过程增添新的维度。然而，即便是目前功能最强大、用途最广泛的机器学习应用，也主要集中在关联分析领域，本质上可归结为复杂的数据拟合。Judea Pearl曾指出，通用人工智能必须涉及包含“执行”与“想象”行为的干预。因此，任何机器辅助的科学发现都必须包含因果分析与干预。在此背景下，我们提出了一种面向物理原理的因果学习模型，该模型不仅能识别相关性，还能揭示因果关系。我们运用因果推断与干预的原理，针对若干著名物理现象研究其因果关系。我们表明，该技术不仅能发现数据间的关联，还能正确确定变量之间的因果关系，从而增强（或削弱）我们对所提出的底层物理过程模型的信心。
英文摘要：
The advances in Artificial Intelligence (AI) and Machine Learning (ML) have opened up many avenues for scientific research, and are adding new dimensions to the process of knowledge creation. However, even the most powerful and versatile of ML applications till date are primarily in the domain of analysis of associations and boil down to complex data fitting. Judea Pearl has pointed out that Artificial General Intelligence must involve interventions involving the acts of doing and imagining. Any machine assisted scientific discovery thus must include casual analysis and interventions. In this context, we propose a causal learning model of physical principles, which not only recognizes correlations but also brings out casual relationships. We use the principles of causal inference and interventions to study the cause-and-effect relationships in the context of some well-known physical phenomena. We show that this technique can not only figure out associations among data, but is also able to correctly ascertain the cause-and-effect relations amongst the variables, thereby strengthening (or weakening) our confidence in the proposed model of the underlying physical process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
