AURA - The Business Expert 

Empowers any user by leveraging enterprise knowledge & know-how

LEO - The Engineer

Effectively solves technical challenges in all engineering disciplines

MARIE - The Scientist

Brings deep scientific knowledge to engineers or researchers

Moment 1: LEO |  Design Performance Analysis

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 in 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.

Moment 2: LEO |  Design Performance Analysis

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 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 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.

Moment 3: LEO |  Design Performance Analysis

Moment 4: LEO |  Design Performance Analysis

Moment 5: LEO |  Design Performance Analysis

Moment 6: LEO |  Design Performance Analysis

Challenge:

Brake caliper natural-frequency and mode-shape evaluation is important for understanding dynamic behavior and assessing brake squeal and noise, vibration & harshness (NVH) performance. Every design change can shift the caliper's modal characteristics, requiring a new FE modal simulation. Repeated simulations require expert FE analysts and can take significant time and computational resources, limiting the number of design variants engineers can evaluate.

Solution: With the Virtual Companion LEO, users can build and run the Modal Analysis for the brake caliper using a simple natural language commands. LEO assists throughout the workflow, starting with material assignment and explaining how to perform the modal analysis. LEO can create the finite element model (FEM) and set up and run the modal simulation as specified by the user. It then visualizes the results and helps the user analyze specific KPIs, such as the extracted natural frequencies, with a simple prompt

Benefit: Using AI accelerates the analysis process, giving users the results they need, faster. From setting up the simulation to processing the results, it democratizes access to simulation, allowing non-experts to get results with a natural language command instead of having to navigate complicated tools.

Moment 7: LEO |  Design Performance Analysis

Challenge:

Setting up simulations can be challenging for users without CAE experience. Settings, boundary conditions and loads all need to be set up, and this requires significant work.

Solution: Rather than having to manually set up the simulation through menus and dialogs, LEO guides users through the simulation setup process via an interactive chat. 

Benefit: With LEO, users can set up simulations without relying on a traditional GUI. This democratizes access to simulation, allowing designers without simulation expertise to take advantage, while also reducing the routine work for experienced analysts, allowing them more time to work on the more important issues.

Moment 1: Generative Physics Behavior Creation

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.

Moment 2: Generative Structural Performance Prediction

Challenge

Physics simulation remains underused in early design - leading to high costs, underperforming products, lengthy design cycles, and material waste. The classical objection is the complexity and required skill for the successful, widespread deployment of simulation in product design.

Solution

The Virtual Twin of Physics Behavior enables Generative Structural Performance Prediction. It puts generative, optimized workflows and performance prediction directly in the designer's hands - demonstrated here for plastic packaging design. Designers can now rapidly evaluate design alternatives to optimize the product directly and independently in near real time.

Benefit

• The impact is dramatic:
  Turnaround time: 3 days (designer-to-analyst handoff) → 20 minutes (designer only), 
• Design iterations evaluated: 1 → 100

In real-world terms, this can save months of development time. and eliminate thousands of tons of plastic waste. With the Virtual Twin of Physics Behavior, you can turn physics-informed design from a rare, costly checkpoint into a routine, everyday part of the design process.

Moment 3: Generative Structural Performance Prediction

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 of 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

Moment 4: Virtual Twin of Physics Behavior | Syringe Example

Challenge

For designers of medical-grade syringes, one of the key performance metrics is the force required to operate the syringe. This consists of the Break Loose force, required to dislodge the plunger from rest, and the Glide Force, required to maintain steady motion of the plunger during the injection process. The operating force must be small enough to ensure usability by a clinician or patient, but not so low as to cause unintentional plunger movement, which can lead to incorrect dose administration. The plunger-stopper system plays a key role in determining the operating force, and a large number of geometric parameters can affect the outcome. 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 of 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 particular design variant. The syringe designer can interactively examine how stopper design affects operating force. By simply changing the parameters, the designer is presented with a prediction of the force history and the contact pressure within a few seconds, and can thus easily explore a wide range of stopper designs to identify a design that meets the operating force constraints. By examining the contact pressure contours on the stopper, he can also verify that the optimal design will not lead to fluid leakage during the injection process.

Benefit

• Accelerate syringe development by speeding up the analysis process and design loops

• Democratize syringe operating force analysis by encapsulating simulation expertise in a trained AI model

• Make better trade-offs and optimize syringe designs by interactively exploring the design space

• Improve medical device reliability and safety and enhance the patient experience

Moment 5: Virtual Twin of Physics Behavior | Pipe Clamp Example

Challenge
The clamps that hold sections of pipe together need to be designed for each specific application, with different requirements each time. Redesigning the same basic clamp for each application wastes engineering time and resources on routine work that could be better used elsewhere, and each new design must be re-simulated from scratch. An approach is needed that accelerates simulation turnaround and reduces computational effort.

Solution
A Virtual Twin of Physics Behavior trained on simulation results provides feedback about the performance of a design, much faster than the full simulation would. The designer can modify the clamp model with a simple intuitive slider interface, and get updated results within moments showing how it performs. The Virtual Twin of Physics Behavior is integrated into the 3DEXPERIENCE platform, giving it full access to the simulation model and all its data, and allowing it to interface directly with the CAD geometry.

Benefits

• Near instant results accelerate product development to reduce time to market
• Expands the design space to increase innovation
• Reduces risk by shifting analysis left
• Integrated directly into modeling and simulation tools on 3DEXPERIENCE

Moment 6: Virtual Twin of Physics Behavior | Bonnet Torsion

Challenge
A car bonnet (hood) needs to be designed for strength and stiffness, while also being lightweight and complying with crash safety regulations. With so many competing design factors, so many possible parameters to change, and so many model variants that may have different requirements, running a full simulation analysis of every design variant can be prohibitively time consuming.

Solution
A Virtual Twin of Physics Behavior trained on simulation results provides quick feedback about the performance of a design. An simple intuitive slider interface allows the user to modify and test a new design within moments, making it possible to compare the performance of different variants.

Benefits

• Accelerate product development to reduce time to market
• Manage the complexity of large product ranges or design variants efficiently
• Reduce risk by shifting analysis left
• Integrate simulation into design workflows efficiently and democratize access to simulation

Moment 7: Virtual Twin of Physics Behavior | Brake Analysis

Challenge

When designing a new product, every design iteration requires full re-simulation and costly wait times. This slows down the development process, limiting the ability of designers and engineers to explore the entire design space and find the best design. Each simulation can require considerable compute time and resources, slowing down the development process with a bottle neck.
Solution

With the AI-powered Virtual Twin of Physics Behavior, the performance of new geometries can be evaluated in seconds, faster and with fewer compute resources than a full simulation requires. Rather than having to build a new simulation model for each design variant, AI - powered by 3DEXPERIENCE- uses the same model, new parameters – zero rebuilds. Thanks to unified modeling and simulation (MODSIM), the Virtual Twin of Physics Behavior fits into existing workflows.
Benefit

Performance KPIs can be predicted by AI in seconds while maintaining a high level of accuracy, eliminating need for a re-simulation. A new design variant can be analyzed in moments, cutting the simulation time and reducing the cost of compute significantly. This turns re-designs from a linear cycle into an interactive process, accelerating design and unlocking innovation.