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Fundamentals of AI/ML for Simulation Engineers

Online Training Course

Fundamentals of AI/ML for Simulation Engineers

18 - 19 November 2025 | Online | 8 hours per day

Berlin: 9:00am / London: 8:00am
New York 3:00pm / Los Angeles: 12:00am

Course language: English

This course is now fully booked. Please get in touch with Roger Oswald (roger.oswald@nafems.org) to inquire about the course dates in the first quarter of 2026.

This intensive two-day training course is designed to equip simulation engineers with a good understanding and practical skills in applying Artificial Intelligence (AI) in their field.

It is designed to be software-agnostic, prioritizing methodologies, and techniques that engineers can apply across various computational platforms.

The course consists of two parts:

  • The first part is an interactive lecture, which introduces the theory and mathematical background behind artificial intelligence.
  • The second part of the course are hands-on exercises, where participants will learn how to create their own Deep Learning models.

Detailed Course Program:

Day 1

Introductory Example: Modeling Compressor Parameters
  • Problem Description
  • Comparison between AI and traditional solution
An Overview of Example Applications in Research
  • Simulation Acceleration
  • Reduced Order Models
Definitions
  • The definition of AI
  • The difference between AI, Machine Learning, and Deep Learning
Overview of Machine Learning methods
  • The basic principles behind all Machine Learning methods
  • Learning Paradigms (Supervised, Unsupervised, Reinforcement Learning)
  • Supervised Algorithms:
    - Random Forest Regression
    - Gradient Boosting
    - Support Vector Machines
  • Classification and Logistic Regression
  • How to handle non-numerical data
Foundations of Deep Learning
  • How Neurons Form the Hypothesis Function
  • Activation Functions
  • Deep Learning Regression
  • Model Training
  • Forward Pass and Backpropagation
  • Loss Functions
  • Optimizers for Deep Learning
  • Measuring Model Quality
  • Overfitting
  • Data Split
  • Layer Types
  • Neuron Architecture
  • Physics Informed Neural Networks
  • Surrogate Modeling
Practical Exercises
  • Building a Deep Learning Model for sensitivity analysis
  • Building a Physics Informed Neural Network

 

Day 2

Example project for a deep learning surrogate model for design optimization
Creating machine learning models from scratch
  • A high-level overview of creating machine learning models
  • Reviewing available data and setting a goal
  • Data preparation
  • The importance of the training, test, validation split
  • Setting the model architecture
  • Choosing optimization algorithms and loss functions
  • Creating PINNs
  • Evaluating model performance
  • Overview of MLOps
  • An overview of tools to create machine learning models
  • Open-source libraries (TensorFlow vs. PyTorch)
  • Application software
Project preparation
  • Reviewing available data
  • Using a preliminary exploratory data analysis to gauge the feasibility of the ML project
  • Defining a modeling target
  • Working in tandem with a simulation project
Data preparation
  • Data transformation: File formats and making data trainable
  • Data cleaning
  • Handling classes and text with vectorization
  • Dimension reduction
  • Feature selection
  • Feature Engineering
Sampling
  • Introduction to data sampling
  • Statistical sampling methods
  • Active sampling
  • Sampling errors
Measuring model performance and validity
  • Performance measures for ML models
Consuming the machine learning model
  • Predictive Tasks
  • Design optimization
Limitations of machine learning models
  • Model biases
  • Limitations of interpolation and extrapolation
  • Model aging