Using AI and Machine Learning to Improve Design Optimization

Key Challenges in Simulation-Driven Design Optimization

Engineering teams encounter several recurring obstacles when scaling optimization workflows. These challenges limit efficiency, increase cost, and slow innovation.

• Data quality and quantity: Accurate metamodels require clean, representative datasets, and insufficient or noisy data reduces predictive reliability.
• Integration with existing workflows: Embedding optimization and AI models into established simulation pipelines often introduces friction and complexity.
• Model validation: Engineers must verify that surrogate models accurately represent underlying physics before trusting optimization results.
• Computational complexity: High-fidelity 2D and 3D simulations demand significant computational resources, limiting iteration speed and scalability.

These constraints make it difficult to fully leverage simulation data across the product development lifecycle.

How Ansys optiSLang AI+ Solves These Challenges

Ansys optiSLang AI+ extends traditional optimization by integrating AI-driven automation and intelligent metamodeling into existing engineering workflows.

• AI-enhanced metamodel generation: Automatically identifies and builds the most accurate surrogate models using advanced algorithms, reducing manual model selection.
• Seamless workflow integration: Embeds directly into simulation environments, enabling optimization without disrupting established processes.
• Automated validation workflows: Ensures surrogate models maintain accuracy through built-in validation and verification techniques.
• Efficient handling of complex models: Reduces dependence on repeated high-fidelity simulations by leveraging AI-driven approximations.

This approach allows engineers to move from brute-force simulation to intelligent, data-driven optimization.

Supporting Multi-Dimensional Engineering Data

Modern simulation workflows generate diverse data types across multiple domains. Ansys optiSLang AI+ supports a wide range of data formats, enabling comprehensive modeling and analysis.

• 0D scalar values: Handles single-value outputs such as pressure drop, efficiency, or temperature.
  1D signals and curves: Processes time-series or frequency-domain data, such as waveform responses.
• 2D field data: Supports images, wavefronts, and planar distributions like stress or temperature fields.
• 3D volumetric data: Analyzes complex spatial datasets such as full-field stress or electromagnetic distributions.

This flexibility allows engineers to build accurate metamodels across multiple physics domains and data structures.

Intelligent Engineering with AI-Driven Optimization

AI-enhanced optimization transforms how engineers explore design spaces and evaluate trade-offs. Instead of manually iterating through design variables, engineers can rely on data-driven insights to guide decisions.

• Pareto front visualization: Identifies optimal trade-offs between competing objectives such as performance, cost, and durability.
• Accelerated what-if analysis: Enables rapid evaluation of design changes without rerunning full simulations.
• Deeper data insights: Uses machine learning to uncover relationships that may not be apparent through traditional methods.
• Web-based development workflows: Facilitates collaboration and deployment through accessible, scalable platforms.

These capabilities allow teams to evaluate more design options in less time while improving overall solution quality.

Real-World Impact: AI-Driven Filter Design Optimization

Engineering teams applying AI-driven optimization have demonstrated measurable improvements in simulation efficiency and product development timelines.

At MANN+HUMMEL, engineers implemented an AI-based optimization strategy using a parameterized model of air filter properties. By combining design of experiments (DOE) with AI-enhanced optimization, the team significantly reduced simulation effort while improving design outcomes.

• Reduced simulation workload: AI-driven models minimized the number of required high-fidelity simulations.
• Faster decision-making: Rapid evaluation of design scenarios enabled quicker iteration cycles.
• Accelerated time to market: Optimized workflows shortened development timelines for advanced filtration technologies.

This example highlights how AI-driven optimization enables scalable, efficient product development.

Expanding AI Across the Ansys Ecosystem

Ansys AI+ extends beyond optiSLang to enhance simulation capabilities across multiple engineering domains. These AI-driven add-ons integrate directly into core solvers to improve performance and usability.

• Structures AI+: Enhances structural analysis with intelligent automation and predictive modeling.
  CFD AI+: Accelerates fluid dynamics simulations through AI-driven optimization and reduced-order modeling.
• Electronics AI+: Improves electromagnetic simulation workflows with data-driven insights.
  Granta AI+: Enables advanced materials intelligence and data management.
• SynMatrix AI+: Supports system-level optimization and design exploration.
• Missions AI+: Enhances mission engineering and system-of-systems analysis.

These tools create a unified AI-enabled simulation ecosystem that supports end-to-end product development.

Overcoming Engineering Workflow Barriers

Engineering teams often face systemic challenges that limit their ability to fully leverage simulation data. Ansys optiSLang AI+ provides targeted solutions to these barriers.

• Tool standardization and orchestration: Integrates optimization tools into a unified workflow, reducing fragmentation.
• High cost of physical testing: Replaces extensive testing with validated simulation-driven models.
• Underutilized test data: Applies machine learning to extract additional value from existing datasets.

By addressing these challenges, teams can maximize the return on simulation investments.

Integrating AI into the Engineering Data Value Stream

Ansys optiSLang AI+ enables organizations to embed AI directly into their product development processes, transforming simulation data into actionable insights.

• Implement optimization within the data pipeline: Integrates seamlessly into existing workflows to enhance efficiency.
• Leverage AI for data science applications: Applies machine learning to improve model accuracy and predictive capability.
• Export and deploy AI models: Allows engineers to integrate optimized models into custom web-based tools and applications.

This approach creates a continuous, data-driven engineering process that supports rapid innovation.

Accelerating Innovation with AI-Driven Simulation

AI-driven simulation enables engineers to shift from reactive analysis to proactive design optimization. By combining high-fidelity simulation with machine learning, teams can explore larger design spaces, reduce computational cost, and improve product performance.

• Faster design cycles: AI reduces the number of required simulations while increasing iteration speed.
• Improved design robustness: Optimization ensures solutions perform reliably under varying conditions.
• Scalable workflows: AI enables efficient handling of complex, multi-physics models.

These advantages position AI-enhanced simulation as a critical tool for modern engineering teams.

Getting Started with Ansys optiSLang AI+

Organizations seeking more efficient optimization workflows can leverage Ansys optiSLang AI+ to enhance simulation capabilities and accelerate product development.

Engineers can evaluate the platform through trial access and integrate AI-driven optimization into existing workflows with minimal disruption. By combining simulation, data science, and automation, teams can reduce development time while improving design outcomes.

For teams facing complex optimization challenges, AI-driven simulation offers a clear path toward faster, more reliable engineering decisions.