This paper presents an overview of scientific modeling and discusses the
complementary strengths and weaknesses of ML methods for scientific modeling in
comparison to process-based models. It also provides an introduction to the
current state of research in the emerging field of scientific knowledge-guided
machine learning (KGML) that aims to use both scientific knowledge and data in
ML frameworks to achieve better generalizability, scientific consistency, and
explainability of results. We discuss different facets of KGML research in
terms of the type of scientific knowledge used, the form of knowledge-ML
integration explored, and the method for incorporating scientific knowledge in
ML. We also discuss some of the common categories of use cases in environmental
sciences where KGML methods are being developed, using illustrative examples in
each category.

科学建模的综述及与基于过程的模型相比，机器学习方法在科学建模中的互补优势和不足；以及科学知识引导的机器学习（KGML）领域的研究现状介绍，旨在利用科学知识和数据在机器学习框架中实现更好的泛化性、科学一致性和可解释性的结果；我们讨论了 KGML 研究的不同方面：使用的科学知识类型、知识 - 机器学习集成的形式以及在机器学习中融入科学知识的方法；同时，我们还讨论了环境科学中 KGML 方法正在开发的常见应用类别，并提供了每个类别的例证。

知识导向的机器学习：当前趋势与未来前景

Knowledge-guided Machine Learning: Current Trends and Future Prospects

Generative machine learning models can use data generated by scientific
modeling to create large quantities of novel material structures. Here, we
assess how one state-of-the-art generative model, the physics-guided crystal
generation model (PGCGM), can be used as part of the inverse design process. We
show that the default PGCGM's input space is not smooth with respect to
parameter variation, making material optimization difficult and limited. We
also demonstrate that most generated structures are predicted to be
thermodynamically unstable by a separate property-prediction model, partially
due to out-of-domain data challenges. Our findings suggest how generative
models might be improved to enable better inverse design.

使用物理引导的晶体生成模型（PGCGM）来作为逆向设计过程的一部分，评估生成式机器学习模型如何利用科学建模生成的数据来创造大量的新材料结构。研究发现，PGCGM 的输入空间对参数变化并不平滑，导致了材料优化的困难和局限性；同时，通过一个独立的属性预测模型表明，大部分生成的结构在热力学上是不稳定的，部分原因是由于领域外数据挑战。研究结果为改进生成模型以实现更好的逆向设计提供了建议。