Online Training Course

Fundamentals of AI for Simulation Engineers

8 - 9 April 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