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