Online Training Course

Fundamentals of AI for Simulation Engineers

4 - 5 June 2025 | Online | 8 hours per day

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

Course language: English

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

T​rainer

M​ax Kassera (yasAI)
Max Kassera studied mechanical engineering with a minor in economics at the University of Kaiserslautern-Landau, where he first applied machine learning and artificial intelligence to turbocharger design in 2017. After graduating, he was awarded two German government grants to develop AI software for mechanical engineering, which led to the incorporation of yasAI in 2022. With yasAI, Max began training engineers in applying AI to simulation projects with a focus on simulations and fluid mechanics.

FAQ:

Do I need previous AI knowledge to attend the course?
No. This course has been specifically designed for simulation engineers who have no prior experience with artificial intelligence.

Will I need to install any software for the practical sessions?
No. We'll provide a virtual environment that you can access directly through your web browser. If corporate IT restrictions block your access, we have several fallback solutions available. Additionally, a member of the yasAI team will always be on hand to offer technical support.

Do I need programming experience for the practical part?
No programming experience is required. All examples are ready-to-use, so you won't need to do any coding. However, if you do have Python experience, our examples have proven to be an excellent starting point for attendees who want to extend their projects.

Are the examples tailored to specific simulation software?
No. All examples provided are software-independent. The course concentrates on fundamental AI concepts, equipping you with the skills needed to apply AI techniques across any simulation software.

Organisation

Duration
8 hours per day, each day 9:00 am - 5:00 pm (UTC+2,Berlin)

Language
English

Course Fee
Non NAFEMS members: 1.550 Euro / person*
NAFEMS member: 1.200 Euro / person*
Included in the fees are digital course notes and a certificate.
* plus VAT if applicable.

NAFEMS membership fees (company)
A standard NAFEMS site membership costs 1,365 euros per year, an academic site and entry membership costs 855 euros per year.

Cancellation Policy
Up to 6 weeks before course starts:
free of charge;
up to one week before: 75 %;
later and no show: 100 %.

Course cancellation
If not enough participants we keep the right to cancel the course one week before. The course can be canceled also in case of disease of the speakers or force majeure. In these cases the course fees will be returned.

Organisation / Contact
NAFEMS
e-mail: roger.oswald@nafems.org

Accreditation Policy

The course is agreed and under control of NAFEMS Education and Training Working Group (ETWG).