Physics-Informed ML for PCB Thermal and EM Simulation

Data generation pipeline for ML-based PCB simulation

Overview

During my research internship at IIT Delhi from May to July 2025, I worked on developing comprehensive datasets for physics-informed machine learning models aimed at PCB (Printed Circuit Board) simulation. This work is part of a larger project to create an indigenous PCB design and simulation software with Generative AI capabilities.

Problem Statement

Traditional PCB simulation using Finite Element Method (FEM) is computationally expensive and time-consuming. Machine learning approaches can potentially speed up simulations, but they require high-quality, diverse training datasets with accurate physics simulations. The challenge was to:

  • Generate cross-validated benchmark datasets for thermal, elasticity, and electromagnetic simulations
  • Ensure physical accuracy and numerical consistency across simulations
  • Create datasets suitable for training physics-informed neural networks
  • Validate results against established simulation tools

Methodology

The project involved building a comprehensive data generation pipeline:

  • FEM Pipeline Development: Implemented and optimized FEM simulation pipelines for multiple physics domains
  • Mesh Generation: Developed robust mesh generation procedures for various PCB geometries and components
  • Multi-Physics Simulation: Generated datasets covering thermal analysis, structural elasticity, and electromagnetic behavior
  • Validation Framework: Cross-validated results using multiple simulation tools (MFEM, OpenEMS, Elmer)
  • Dataset Curation: Organized and structured datasets for machine learning consumption

Technologies

  • FEM Tools: MFEM (Modular Finite Element Methods), OpenEMS (EM simulation), Elmer (multi-physics)
  • Mesh Generation: Gmsh, custom meshing tools
  • Scientific Computing: Python, NumPy, SciPy
  • Data Management: pandas, HDF5 for large dataset storage
  • Validation: Custom validation scripts comparing results across multiple solvers

Outcomes

Successfully created:

  • A comprehensive benchmark dataset covering thermal, mechanical, and electromagnetic simulations
  • Validated FEM pipeline with cross-tool verification ensuring physical accuracy
  • Structured dataset format suitable for training physics-informed machine learning models
  • Documentation and tools for extending the dataset to new PCB designs and configurations

This dataset serves as a foundation for developing ML models that can accelerate PCB simulation while maintaining physical accuracy, potentially reducing simulation time from hours to seconds for common PCB design tasks.