Skip to Main Content

Optimize Your Simulation with Ansys Mechanical and Ansys optiSLang

Join us for this second episode of the webinar series to learn how Ansys optiSLang connects to best-in-class Ansys Simulation in the mechanical engineering discipline to achieve effective design exploration and optimization. This means optimizing a design with minimal effort thanks to parametric variation analysis and advanced automation capabilities. In the background, Ansys optiSLang leverages Artificial Intelligence (AI) and Machine Learning (ML) methods to build optimal prediction metamodels and explore the design space better and faster.

Don’t miss the upcoming episode to hear from Ansys experts and ask your questions.


Watch Presentation

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

About this Webinar

Engineers make trade-offs between size, performance, and reliability in engineering product development. Simulation provides informed decisions early in the design cycle.

This episode starts with a structural finite element model in Ansys Mechanical, involving multiple design parameters and focusing on a multi-objective optimization problem. Instead of manually tuning parameters and running tedious iterations in a trial-and-error approach, we will demonstrate how optiSLang parametric workflow directly from Ansys Workbench helps to automate and streamline the optimization process. Optimization and sensitivity studies utilize the latest AI and machine learning methods in optiSLang and include linear, nonlinear, and thermal FEA problems. We’ll cover advanced topics, such as expanding native parametrization using Python scripting. We’ll demonstrate the workflow on a representative issue aligned with typical industrial problems.

What You Will Learn

  • Explore the design space with advanced sensitivity analysis
  • Make the most use of the simulation Design of Experiment and using it as the building block for:
    • Optimization with a minimal learning curve
    • Creating a metamodel of optimal prognosis (surrogate model) and finding optimal design faster
    • Accelerating innovation by minimizing non-value-added and repetitive tasks, and fully automating the simulation workflows 

Who Should Attend

Mechanical (Simulation) Engineers & Analysts in all industries + Academia


Saeed Jahangirian, Principal Application Engineer at Ansys

    Adarsh Chaurasia, Lead Application Engineer at Ansys

    Mechanical engine block mesh