Isight is SIMULIA’s industry-leading solution for simulation process automation and design optimization. It enables engineering teams to create automated, repeatable, and scalable workflows that integrate a wide range of simulation tools, CAD systems, custom scripts, and data analysis methods.
Paired with the SIMULIA Execution Engine (SEE), Isight supports distributed execution across local and cloud resources—accelerating simulation cycles and reducing design time.
Why Use Isight?
Automate Complex Simulation Workflows
- Link CAE, CAD, CFD, and in-house tools into a single drag-and-drop process flow
- Automate simulation runs, data transfer, and parameter control
- Reduce human error and eliminate repetitive manual tasks
Optimize Product Designs Efficiently
- Perform parametric studies, Design of Experiments (DOE), and multidisciplinary optimization (MDO)
- Identify optimal design configurations with multi-objective optimization techniques
- Support robust design strategies like Design for Six Sigma (DFSS) and Monte Carlo Simulation
Accelerate Innovation and Reduce Costs
- Evaluate hundreds of design alternatives with minimal manual effort
- Minimize physical prototyping and reduce time-to-market
- Reuse workflow templates across projects and teams for consistent processes
Key Capabilities of Isight
Visual Workflow Modeling
Intuitive, GUI-based environment with drag-and-drop process components
Component Library
Integrates tools like Abaqus, Ansys, MATLAB, Excel, Python, Fortran, and custom scripts
Advanced Design Exploration
Built-in support for DOE, global/local optimization, response surface modeling, and sensitivity analysis
Data Management and Real-Time Visualization
Automatically stores results; view 2D/3D plots, correlation matrices, and performance trends
Cross-Platform Scalability
Execute workflows locally or across HPC clusters via SIMULIA Execution Engine
SIMULIA Execution Engine (SEE)
SEE enables centralized and distributed execution of simulation workflows, empowering engineering teams to:
- Distribute and parallelize large simulations across compute nodes
- Securely share workflows across departments or global teams
- Scale workflow execution without additional software reconfiguration
Business Benefits
- Faster Design Cycles – Automate and parallelize complex tasks
- Better Product Reliability – Explore more alternatives with less manual effort
- Lower Development Costs – Reduce reliance on physical testing and manual iteration
- Greater Reusability – Build a library of reusable workflows for future programs
- Cross-Disciplinary Collaboration – Integrate mechanical, thermal, control, and cost models in one process
Key Components of Isight Design Exploration and Optimization
Design of Experiments (DOE)
Systematically explore and understand your design space.
- Evaluate the sensitivity and influence of design variables
- Identify interactions between variables to guide optimization strategies
- Generate structured datasets to support surrogate modeling and optimization workflows
Civil Engineering and Infrastructure
Automate performance improvement with advanced optimization strategies.
- Access a wide range of optimization methods: gradient-based, global, multi-objective, pattern search, and autonomous strategies
- Combine DOE, optimization, and response surface modeling for robust design exploration
- Solve constrained, nonlinear, and multidisciplinary problems across domains
Data Matching
Calibrate simulation models to match real-world test results.
- Compare simulation outputs against experimental or target data
- Minimize discrepancies and improve model accuracy
- Improve design confidence with data-driven validation
Approximation Modeling and Visual Design Driver
Accelerate insight through real-time predictive modeling.
- Build surrogate models (response surfaces, regression models) for fast evaluations
- Use Visual Design Driver to interactively explore the design space
- Enable design reviews and iterations without full simulation reruns
Quality and Robustness Techniques
Incorporate uncertainty to build more resilient designs.
- Use probabilistic and statistical methods to simulate operating variability
- Run Monte Carlo simulations to evaluate the impact of uncertainty on system performance
- Identify critical parameters influencing product quality and reliability
