AURA - the Business Expert 

Empowers any user by leveraging Enterprise Knowledge & Know-How

LEO - The Engineer

Effectively solve technical challenges in all engineering disciplines

MARIE - The Scientist

Bring Deep Scientific Knowledge to Engineers or Researchers

Challenge

Designers and Design Engineers still rely on their own experience and trial-and-error to configure simulation setups, often without deep expertise in advanced physics tools. Simulation workflows have a steep learning curve, and enterprise best practices are rarely embedded into the process itself — they live in people's heads or scattered documentation instead. As a result, converting design goals and KPIs into properly configured simulations requires significant manual effort, with little guided automation or contextual intelligence to assist along the way. This leads to inefficiencies, delays, and frustration, slowing down design decisions and increasing the risk of errors and rework

Solution

LEO Design Performance Analysis empowers you with advanced physics insight through intuitive natural-language interaction across structures and motion disciplines. It generates design performance insights and physics KPIs, transforming simulation setup and results exploration into a guided, conversational experience that flags what matters in the results and helps engineers configure the right simulation for the question they're asking. By leveraging best-practice knowledge and natural-language interaction, LEO Design Performance Analysis lowers the barrier to simulation expertise, enabling engineers, regardless of prior simulation experience, to set up, run, and analyze simulation results with confidence.

Benefit

• Turn engineering intent into KPI-driven results faster across structural and motion workflows — linear static, modal, and motion analyses.
• Confidently evaluate design alternatives, following a consistent simulation workflow regardless of prior physics expertise.
• Move faster from simulation setup to design decisions across structural and motion workflows, with less manual effort.

Challenge

In complex simulation tasks, just managing the model and results can be a significant undertaking. Users need AI to handle complexity and perform tasks automatically, streamlining the process for users and allowing them to focus on more important work. But to do this, the AI needs access to all the relevant data and context in one place.

Solution

With a few typed natural language prompts, LEO can answer questions about the model, change the visualization and help the user to validate the simulation scenario. Users can explore and manage all the relevant data with AI assistance, replacing complex workflows with simple natural prompts.

• Summarization and overviews: LEO can provide an overview of the project, including the objectives of the project, the components that make up the system, the KPIs to be analyzed, and simulation-specific data such as mesh count and boundary settings.
• Visualization: The user can specify how they would like to view the model - for example, to show the mesh for structural simulation. This can be done with a simple request, rather than manually having to find the correct options in menus.
• Scenario validation: LEO can output data such as mesh quality, material properties and scenario settings in a clear, readable format. Potential problems can be highlighted and explored with LEO's assistance.
• Results exploration: With a simple prompt, LEO can process and display the results automatically, without the user having to specify manually.

Benefit

• Simulations can be validated and analyzed faster, reducing time to market.
• Natural language interface means users don't need to know how to use different tools, democratizing the process and reducing the skill barrier to using simulation.

Challenge
Simulation tools remain locked to experts — requiring deep expertise and significant time to set up and run. Other physics AI tools operate in isolation: hard to scale, and disconnected from your organization's broader product lifecycle.

Solution
Generative Physics Behavior Creation delivers an end-to-end, design-integrated environment for training and deploying AI physics behavior — grounded in validated physics, not just approximation — for every user. It gives your organization the capability to deliver high-performance products at speed.

Benefit

• Built for the full lifecycle, not just design-stage iteration — physics behavior is already connected to design, certification, and IP traceability, with manufacturing and 'in use' integration on the roadmap — extending trust and reuse well beyond the concept phase.
• Enterprise-grade traceability, built in — direct integration with IP Management ensures complete transparency and provenance of every data source, so your organization always knows exactly what your AI physics behavior was trained on and why it can be trusted.
• Seamlessly integrated into your enterprise design and simulation environment, leveraging existing physics results data with no added friction.
• Smarter training, better data — explore wide ranges of training data in 3D to identify and eliminate outliers, improving training quality and model reliability.

Challenge
Physics simulation remains underused in early design — leading to high costs, underperforming products, lengthy design cycles, and material waste.

Solution
Generative Structural Performance Prediction puts generative, optimized workflows and performance prediction directly in the designer's hands — demonstrated here for plastic packaging design. It shifts simulation from a rare, specialist-only, disconnected step into a fast, repeatable capability designers use directly, evaluating many design alternatives rapidly to optimize the product in real time.

Benefit
The impact is dramatic:

• Design iterations evaluated: 1 → 100
• Product validation time: 2 hours → 20 minutes
• Turnaround time: 3 days (designer-to-analyst handoff) → 20 minutes, with a single user

In real-world terms, this saved 12 months of development time, cut material usage from thousands of bottles a year to hundreds, and eliminated 1,500 tonnes of plastic waste from first engagements alone — turning physics-informed design from a rare, costly checkpoint into a routine, everyday part of the design process.

Challenge
Every time a design changes, the analysis needs to begin again from scratch. For complex products, updating the model and re-running the simulation to analyze each variant or design iteration can take a significant time, slowing the development process.

Solution
The Virtual Twin Physics Behavior is an AI-powered Virtual Twin, built on robust physical simulation data from SIMULIA tools, which can quickly predict the performance of a design. Users can adjust design parameters interactively and get extremely fast feedback on the impact of a design change.

Benefit

• Accelerate product development by speeding up the analysis process and design loops
• Democratize analysis by encapsulating simulation expertise in a trained AI model
• Make better trade-offs and optimize designs by interactively exploring the design space