让物理模型与黑箱模型协同时，防止错误物理被掩盖。
Structural Constraints for Physics-augmented Learning
- 用两个约束条件确保混合模型不扭曲物理规律
- 黑箱无法复现纯物理模型，且参数一致
- 适合需保证物理可信性的工程建模场景
当物理规律错误时，物理信息机器学习会变成误导性学习。强大的黑箱模型不应能掩盖错误的物理认知。我们提出两项标准以确保混合（物理+黑箱）模型的完整性：0）黑箱模型无法复制物理模型；1）最优拟合的混合模型与最优拟合的纯物理模型具有相同的物理参数。我们在一个通过小信号线性化近似的非线性机械系统上进行了验证。
原文摘要 · Abstract (English)
When the physics is wrong, physics-informed machine learning becomes physics-misinformed machine learning. A powerful black-box model should not be able to conceal misconceived physics. We propose two criteria that can be used to assert integrity that a hybrid (physics plus black-box) model: 0) the black-box model should be unable to replicate the physical model, and 1) any best-fit hybrid model has the same physical parameter as a best-fit standalone physics model. We demonstrate them for a sample nonlinear mechanical system approximated by its small-signal linearization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。