Recent advances in computational infrastructure and large-scale data processing have accelerated the adoption of data-driven inference methods, particularly deep learning (DL), to solve problems in many scientific and engineering domains. In wireless systems, DL has been applied to problems where analytical modeling or optimization is difficult to formulate, relies on oversimplified assumptions, or becomes computationally intractable. However, conventional DL models are often regarded as non-transparent, as their internal reasoning mechanisms are difficult to interpret even when model parameters are fully accessible. This lack of transparency undermines trust and leads to three interrelated challenges: limited interpretability, weak generalization, and the absence of a principled framework for parameter tuning. Science-informed deep learning (ScIDL) has emerged as a promising paradigm to address these limitations by integrating scientific knowledge into deep learning pipelines. This integration enables more precise characterization of model behavior and provides clearer explanations of how and why DL models succeed or fail. Despite growing interest, the existing literature remains fragmented and lacks a unifying taxonomy. This tutorial presents a structured overview of ScIDL methods and their applications in wireless systems. We introduce a structured taxonomy that organizes the ScIDL landscape, present two representative case studies illustrating its use in challenging wireless problems, and discuss key challenges and open research directions. The pedagogical structure guides readers from foundational concepts to advanced applications, making the tutorial accessible to researchers in wireless communications without requiring prior expertise in AI.

翻译：近年来，计算基础设施和大规模数据处理技术的进步加速了数据驱动推理方法（尤其是深度学习）在众多科学与工程领域问题求解中的应用。在无线系统中，深度学习已被应用于那些难以建立解析模型或优化公式、依赖过度简化假设、或计算上难以处理的问题。然而，传统的深度学习模型通常被视为非透明的，因为即使模型参数完全可获取，其内部推理机制也难以解释。这种透明度的缺失削弱了信任度，并导致三个相互关联的挑战：有限的可解释性、较弱的泛化能力，以及缺乏参数调优的原则性框架。科学启发的深度学习作为一种有前景的范式应运而生，它通过将科学知识整合到深度学习流程中来应对这些局限性。这种整合使得模型行为能够得到更精确的表征，并为深度学习模型成功或失败的原因提供更清晰的解释。尽管关注度日益增长，现有文献仍较为零散且缺乏统一的分类体系。本教程对科学启发的深度学习方法及其在无线系统中的应用进行了结构化综述。我们提出了一个组织科学启发深度学习研究版图的结构化分类体系，展示了两个说明其在具有挑战性的无线问题中应用的典型案例研究，并讨论了关键挑战与开放研究方向。本教程的教学结构引导读者从基础概念逐步深入到高级应用，使得无线通信领域的研究人员无需具备人工智能的先验专业知识即可理解内容。