Online Training Course

Fundamentals of AI for Simulation Engineers

11 - 12 December 2024, Online

ATTENTION:
T​his course is now fully booked! We will schedule another course in Q1/2025. To secure your place, please send an e-mail with your contact data to roger.oswald@nafems.org.

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 offers a balanced mix of theoretical knowledge and hands-on applications, ensuring participants gain a robust foundation in AI and its relevance to simulation engineering.

Detailed Course Program:

Day 1

• Introductory example
  - Problem description: Modeling compressor parameters
  -​ Traditional approach vs. Artificial Intelligence
  - Explanation: Why AI outperformed human intelligence

• Overview of AI modeling techniques
  -Explaining the difference between Artificial Intelligence, Machine Learning, and Deep Learning
  - The basic principles behind all AI systems
  - Categories of machine learning
  - Supervised learning
  - Unsupervised learning
  - Semi-supervised learning
  - Reinforcement learning
  - Types and examples of different supervised Machine Learning Algorithms
  - Linear & logistic regression
  - Decision Tree & Random Forest
  - Gradient Boost Algorithms
  - Support Vector Machines
  - Neural Networks & Deep Learning
  - The different input types of machine learning algorithms
  - Numerical data
  - Classes and vectorized data
  - Images
  - Text
  - Different applications of machine learning algorithms
  - Surrogate modeling
  - Classification
  - Generative AI
• Introduction to training deep neural networks
  - Neurons and activation functions
  - Forward pass and backpropagation
  - Layers and layer types
  - The influence of topology on neural networks
  - Hyperparameters and hyperparametertuning
  - Loss functions
  - Optimization algorithms to train model parameters
  - In-depth explanation: Gradient Descent
  - Comparison of common algorithms
  - Deciding which algorithm to use

• Physics-Informed Neural Networks (PINN)
  - How PINNs work
  - Advantages
  - Performance comparison
  - Pitfalls

• Example application of a surrogate model

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.

Organisation

Duration
Day 1: 9:00 am - 5:00 pm
Day 2: 9:00 am - 5:00 pm
Login phase from 8:30 am.
Time zone: CET (Central European Time), UTC+1 (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).