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Advanced LS-OPT
Deterministic and
Probabilistic Optimization

Course Overview

This course is intended to help engineers with a basic knowledge of LS-DYNA and LS-OPT to become proficient in advanced optimization and probabilistic design methods. With this course we hope for you to become more productive at design and parameter identification of complex systems, such as multidisciplinary systems with competing objectives, advanced material testing and models, and systems with discontinuous responses. We will also provide insight into reliability and robustness to facilitate higher quality product design. Additionally, we will introduce classification-based adaptive sampling constraints as a tool for enhancing the efficiency.

In this course, we will discuss both the theoretical and practical aspects of design. We will cover advanced topics, such as multi-objective and collaborative optimization, digital image correlation, statistical classification, and probabilistic optimization. During workshop sessions, we will apply the discussed theoretical topics. We will use the LS-OPT graphical user interface to teach input preparation and post-processing. We will also emphasize interfacing with LS-DYNA.


  • Required: Basic knowledge about direct and metamodel-based optimization and result analysis using LS-OPT.
  • Strongly recommended: Introduction to LS-OPT class since it provides a foundation for some of the advanced topics.
  • Recommended but not required: An introductory class in LS-DYNA for familiarity with a few keywords. 

Teaching Method

The teaching method is simple and practical to reduce the barrier to learn advanced topics. The class relies on simple class-room introduction of advanced topics in simple and incremental way to make it easier to understand all topics.

Learning Outcome

Following completion of this course, you will be able to:

  • Calibrate unknown material model and system parameters using Digital Image Correlation (DIC).
  • Perform multi-objective and collaborative optimization.
  • Use classification-based constraint approximation to handle discontinuous and binary responses, and to perform adaptive sampling.
  • Perform reliability analysis using Monte Carlo analysis.
  • Perform probabilistic design and tolerance optimization to improve product reliability and robustness.
  • Identify outliers and sources of uncertainty in design.

 Available Dates

Currently, no training dates available

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. Please contact us.


This is a 3-day classroom course covering both lectures and workshops. For virtual training, this course is covered over 5 x 2-hour sessions (lectures only).

Virtual Classroom Session 1 / Live Classroom Day 1

  • Module 1: LS-OPT overview and introduction to optimization
  • Optimization fundamentals
  • Direct optimization
  • Metamodel-based optimization
  • Metamodel accuracy and error analysis
  • LS-OPT interface
  • Metamodel-based optimization strategies
  • Workshop 1.1: Simple metamodel-based optimization and results

Virtual Classroom Session 2 / Live Classroom Day 1

  • Module 2: Full field material parameter estimation
  • Introduction
  • Digital image correlation
  • Curve distance measures
  • Workshop 2.1: Ordinate-based mean square error (optional)
  • Workshop 2.2: Ordinate-based MSE for multiple cases (optional)
  • Workshop 2.3: Hysteretic response- Multiple cases (optional)
  • Workshop 2.4: GISSMO failure model- Shear load case (optional)
  • Workshop 2.5: Full field calibration (DIC)
  • Module 3: Classification-based constraint handling
  • Classifiers for optimization
  • Adaptive sampling
  • LS-OPT interface
  • Workshop 3.1: Sequential optimization with classifier-based design constraint
  • Workshop 3.2: Sequential optimization with classifier-based sampling constraint
  • Workshop 3.3: Sequential optimization with domain reduction & classifier-based design constraint

Virtual Classroom Session 3 / Live Classroom Day 2 

  • Module 4: Multidisciplinary, multi-level, and multi-objective optimization
  • Optimization of multiple disciplines
  • Handling shared variables
  • Nested optimization
  • Optimization of conflicting objectives using genetic algorithm
  • Workshop 4.1: Multidisciplinary optimization
  • Workshop 4.2: Multilevel optimization
  • Workshop 4.3: Direct Multi-objective optimization
  • Workshop 4.4: Metamodel-based Multi-objective optimization

Virtual Classroom Session 4 / Live Classroom Day 2

  • Module 5: Probabilistic analysis and optimization
  • Uncertainty introduction
  • Statistical distributions
  • Direct and metamodel-based Monte Carlo analysis
  • LS-OPT interface and results
  • Sequential probabilistic analysis
  • Reliability-based design optimization
  • Robust design optimization
  • Tolerance optimization
  • DynaStats
  • Outlier analysis
  • Metal forming and random fields

Virtual Classroom Session 5 / Live Classroom Day 3 

  • Workshop 5.1: Direct Monte Carlo analysis
  • Workshop 5.2: Metamodel-based Monte Carlo analysis
  • Workshop 5.3: Classifier-based Monte Carlo analysis
  • Workshop 5.4: Sequential metamodel and classifier-based Monte Carlo analysis
  • Workshop 5.5: Adaptive sequential Monte Carlo analysis using classifier sampling constraints
  • Workshop 5.6: Reliability-based design optimization
  • Workshop 5.7: Robust design optimization
  • Workshop 5.8: Reliability analysis using imported metamodel
  • Workshop 5.9: Tolerance optimization
  • Workshop 5.10: DynaStats for direct Monte Carlo
  • Workshop 5.11: DynaStats for metamodel-based Monte Carlo
  • Workshop 5.12: Outlier analysis
  • Workshop 5.13: Metal forming reliability analysis
  • Optimization fundamentals
  • LS-OPT interface- Direct optimization
  • Metamodel-based design optimization
  • Metamodel accuracy and error analysis
  • LS-OPT interface
  • Metamodel-based optimization strategies
  • Full-field material parameter estimation
  • Parameter identification
  • Digital image correlation (DIC)
  • Classification-based design optimization
  • Multi-objective, multidisciplinary, and multilevel optimization
  • Probabilistic analysis and optimization
  • Direct, metamodel-based, and classifier-based reliability analysis
  • Reliability-based design optimization
  • Robust parameter design
  • Tolerance optimization
  • LS-DYNA spatial statistics
  • DynaStats
  • Outlier analysis
  • Metal forming
  • Uncertainty quantification using random fields.