AI-Supported Simulation for Development and Production

Decisions in development and production often must be made before their full implications are fully foreseeable. Simulation reduces this uncertainty but is often time-consuming and computationally intensive. It quickly reaches its limits, especially with complex parameter spaces.

This is precisely where AI-powered simulation comes in: It accelerates calculations, expands the search space, and opens up entirely new fields of application. These include automated optimization and real-time process monitoring.

Simulation is a key tool throughout the entire industrial value chain. It supports companies in product development, production planning, and day-to-day operations.

In development and operations, CFD simulations and FEM simulations enable evaluating flow behavior, heat transfer, or mechanical stresses at an early stage and designing components optimally. In production, simulations are used to test material flows, plant designs, or automation solutions risk-free.

The combination of AI and simulation opens up new possibilities for understanding and optimizing complex systems. This can be achieved through various approaches:

• Synthetic data generation: Simulations generate large, labeled datasets for training AI models
• AI training via simulation: AI models (e.g., reinforcement learning) are trained in simulated environments and transferred to real-world systems
• AI-assisted determination of simulation boundary conditions: Required boundary conditions that are difficult to determine can be specified more precisely (e.g., forces or heat transfer coefficients)
• Implementation of physical laws in AI models: Development of physics-informed AI models (e.g., PINNs)
• Replacing traditional simulations: Using surrogate models, AI learns the behavior of computationally intensive simulations and replaces them with fast approximations

In practice, the widespread use of simulation often fails due to the high modeling effort, limited expert resources, and long computation times. In addition, parameter studies and optimizations are computationally intensive and yield only a fraction of the possible solutions. By combining traditional simulation methods with AI, typical bottlenecks in today’s simulations are overcome. 

✔ Cost reduction and fast ROI

Fewer physical prototypes, reduced computational effort, and automated processes immediately lower development and operating costs. The return on investment can be measured promptly along the entire value chain.

✔ Shorter time-to-market

Thousands of variants are evaluated in a fraction of the time previously required. Faster iterations and earlier, reliable decision-making foundations measurably shorten development cycles—a direct competitive advantage.

✔ Confidence in decision-making and risk minimization

Objective, data-driven analyses replace gut feelings. Comprehensive virtual testing—including of edge cases—reduces technical and economic risks before real-world deployment.

✔ Real-time process optimization

Used where traditional simulation is too slow. Digital twins and real-time process monitoring enable the minimization of effort and resource usage through faster calculations in AI models.

✔ Higher model quality

By using AI to more precisely estimate simulation parameters that are difficult to determine from data, or by directly integrating physical laws into adaptive models, more reliable predictions can be achieved even with limited data

AI-supported simulation produces models that deliver results faster, make more accurate predictions, and are significantly more flexible in digital production.

The first step into AI-supported simulation is typically taken through a clearly defined proof of concept (PoC), which can be economically planned and implemented with manageable risk.

The investment depends primarily on the model’s complexity, the scope of integration, and the available data set. Typical projects start at 14,400 euros for a PoC that includes exploratory data analysis.