Machine Learning for EMC/SI/PI – Blackbox, Physics Recovery, and Decision Making

Machine learning (ML) is one of today's most studied subjects in almost every research area. It provides interesting mathematical tools that could inspire us to rethink about the EMC/SI/PI engineering. This paper gives a preliminary review on machine learning methods for EMC/SI/PI technology developments. Sample examples from publications on EMC/SI/PI methodologies powered by machine learning methods are discussed. There are three major types of machine learning methods. From an EMC/SI/PI engineering point of view, supervised learning provides heterogeneous high dimensional surrogate blackbox model, unsupervised learning enables dimension reduction for physics recovery, and reinforcement learning uses rule-based decision making for optimizations. It is important to select proper machine learning tools and algorithms for various EMC/SI/PI tasks.