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Ansys optiSLang Getting Started (Self-paced
Learning Available)

Course Overview

The Getting Started course offers you an introduction to the practical application of CAE-based sensitivity analysis, multidisciplinary optimization and robustness evaluation with optiSLang
During the training you will be informed about the possibilities of process integration and variation analysis. By means of illustrative examples, sensitivity analyses and different optimization steps as well as their results will be explained and evaluated. Here, the focus is on the user-friendly application and operation of the software.


  • A technical education is recommended and a background in design optimization is not required.
  • This course is designed for users who have little to no experience with Ansys optiSLang.

Teaching Method

Lectures and computer practical sessions to validate acquired knowledge. A major emphasis is placed on teaching by software demonstration and on the development of a solution to a design challenge from start to finish.

Learning Outcome

Module 1: Graphical user interface and process integration

  • Introduction to Ansys optiSLang and the graphical user interface
  • Automate manual simulation steps to conduct parametric variation analyses

Module 2: Sensitivity analysis

  • Sensitivity analysis that helps you to understand your numerical task
  • Investigate parameter sensitivities, reduce complexity, and generate best possible meta models
  • Analysis of experimental data

Module 3: Single- and multi-objective optimization

  • Introduction to optimization goals and constraints
  • Response surfaces based and direct optimization
  • Single- and multi-objective optimization

Available Dates

Date/Time Duration Event Type Location Language Course Cost Registration
15:00 - 17:00
3 Sessions
10-Sep-24 to 12-Sep-24
Virtual VIRTUAL EU English Subscription Only

Learning Options

Training materials for this course are available with an Ansys Learning Hub Subscription. If there is no active public schedule available, private training can be arranged. 

Self-paced Learning 

Complete a class on your own schedule at your own pace. Scope is equivalent to Instructor led classes. Includes video lecture, workshops and input files. All our Self-Paced video courses are only available with an Ansys Learning Hub subscription.


This is a 1-day classroom course covering both lectures and workshops. For virtual training, this course is covered over 3 x 2-hour sessions lectures only.

Virtual Classroom Session 1

Process integration and Graphical User Interface as the first steps towards a parameter study

  • Lecture: Parametrization
  • Demonstration: optiSLang Graphical User Interface (GUI)
  • Lecture and Demonstration: Interfaces to common solvers (Text based, Python) 
  • Lecture and Demonstration: Interfaces to ANSYS (Workbench plugin and Workbench node) 
  • Demonstration: Analytical nonlinear function (Text based + Python + Workbench)
  • Workshop: Kursawe function (Python)

Virtual Classroom Session 2

Sensitivity Analysis

  • Lecture: Design of experiments
  • Lecture: One-dimensional correlations
  • Lecture: Response Surface Method 
  • Lecture: Meta-model of Optimal Prognosis (MOP) and Best Practices
  • Lecture: Adaptive MOP
  • Lecture: Metamodel of optimal Prognosis
  • Demonstration: Usage of the Sensitivity Wizard
  • Lecture/ Demonstration: interpretation of a sensitivity analysis of analytical nonlinear function (Text based + Workbench)
  • Workshop: Sensitivity of Kursawe function (Python)

Virtual Classroom Session 3

Optimization studies

  • Lecture: Single objective, constraint optimization
  • Gradient-Based Methods (e.g. NLPQL)
  • Adaptive Response Surface Methods (e.g. ARSM)
  • Nature-Inspired Optimization (e.g. Evolutionary Algorithm)
  • Multi objective & Pareto optimization (e.g. Evolutionary Algorithm)
  • Demonstration of all steps: Process integration, Sensitivity study and optimization Kursawe function (Python) with different Algorithms
  • Workshop: Single Objective Optimization of Analytical nonlinear function (Text based)