Innovation in engineering thrives where scientific precision and practical approach meet creativity. That spirit came alive at MathWorks AI Day in Heidelberg, where experts from MathWorks and global AI development experts—including our colleagues from N-iX-explored the practical application of MATLAB AI and Simulink AI. If you're looking for practical use of AI for automotive, manufacturing, robotics, and any safety-critical solutions, you've landed on the right page.
As MATLAB's certified partner for System Integration Services, N-iX also participated in the event, sharing insights on real-world AI implementations and integration practices. Here are a few things you should consider if you want to accelerate the move from simulation to certified, production-ready solutions.
MathWorks AI Day bridged concepts with practical applications. It clearly showed how organizations can solve their challenges with AI-enhanced workflows.
Insight 1: AI MATLAB helps build intelligent systems faster
AI MATLAB empowers engineers to enhance design processes, deploy models on embedded systems, and comply with strict safety regulations. Constanze Ruf, Application Engineer at MathWorks, highlighted three key use cases that reflect where AI is making the biggest impact.
- Virtual sensor modeling. AI models can act as virtual sensors, estimating signals that physical sensors fail to measure directly. For example, Gotion used MATLAB AI to estimate a battery's state of charge, achieving high accuracy while minimizing memory and execution time, which is suitable for embedded systems and devices.
- AI-based reduced-order modeling (ROM). Engineers accelerate testing dramatically by replacing heavy FEA or CFD simulations with lightweight surrogate models. A case in point, Subaru engineers used MATLAB Simulink AI to optimize a transmission hydraulic system, reducing calculation time by 99 percent.
- Reinforcement learning in Simulink. With Simulink AI, reinforcement-learning agents are trained through simulated environments before real-world deployment. MathWorks presented Krones AG and Vitesco Technologies cases, where this approach improved manufacturing control and powertrain efficiency.
These applications reveal the growing maturity of the MATLAB AI ecosystem. As N-iX experts noted, the Deep Learning Toolbox integrates seamlessly with PyTorch, TensorFlow, and ONNX, enabling engineers to combine classical modeling precision with flexible AI workflows.
Insight 2: MATLAB Simulink AI allows for building more reliable models by bridging physics and AI
Traditional data-driven AI models often struggle when data is limited, noisy, or doesn't capture all real-world conditions. This is a common issue in safety-critical domains like automotive or aerospace. These models may produce results that fit the data statistically but violate known physical laws (for example, predicting energy outputs or fluid dynamics that are physically impossible). The challenge can be solved by using Physics-Informed Machine Learning (PIML) in MATLAB AI and Simulink AI.
As Dr. Alexander Dirmeier, a Senior Training Engineer at MathWorks, presented, PIML embeds physical equations, chemistry, circuit modeling, and system constraints directly into neural networks. It ensures that predictions remain both scientifically valid and explainable, even with sparse data. This approach allows for developing and training faster, more reliable models for complex, safety-critical systems for automotive, aerospace, pharmaceutical, and other industries.
Explore more: AI in the automotive industry: Fueling a smarter, safer driving experience
Insight 3: Safer automation with Model-Based Design in MATLAB AI and Simulink for robotics
Automotive manufacturers faced challenges in automating complex assembly processes, such as EV battery pack production, while ensuring functional safety and human-machine collaboration. Traditional automation systems struggled to maintain precision and safety when humans worked alongside robots, often requiring costly manual oversight or system shutdowns to prevent accidents. Integrating real-time perception, risk assessment, and adaptive control into one coherent workflow remained a major obstacle. At MathWorks AI Day, Dr. Bogdan Tanygin, a Client Partner at N-iX, demonstrated a hybrid robotic manufacturing solution developed for automotive clients.
The solution integrates Digital Twin and Digital Shadow technologies with Model-Based Design in MATLAB and Simulink and real-time robotic control through ROS 2. It automates EV battery pack assembly while keeping human operators in the loop. Functional safety is ensured via AI-based perception, sensor fusion, and adaptive risk handling, all synchronized within the Digital Twin.
The integration of Digital Twin and Digital Shadow with AI and ML offers a practical path for robotics and hybrid manufacturing. It meets stringent safety requirements and extends across Industry 4.0 scenarios.
Explore further: AI in robotics: 15 use cases, implementation process, and best practices
The landscape of model-based engineering is changing. How can N-iX help you adjust?
Across Europe, enterprises are taking a pragmatic approach to Artificial Intelligence. As Andrzej Bedychaj, a Software Engineer at N-iX, explained, organizations are no longer experimenting for experimentation's sake. They want to develop AI systems that are mathematically verifiable, certifiable, and ready for production.
With Simulink AI and Embedded Coder, combined with mathematically precise methods, we're seeing highly promising use cases for automotive and pharmaceutical industries. Physics-informed AI models help manufacturers demonstrate verifiable compliance within EU safety regulations.
This mindset defines why technologies such as MATLAB AI and Simulink AI are gaining traction among companies that must balance innovation with regulatory rigor. Yet, to translate these tools into tangible business value, enterprises need more than software—they need the right integration expertise.
As a MathWorks System Integration Partner, N-iX helps enterprises integrate MATLAB AI into real-world industrial environments. Our teams bring deep AI and Machine Learning development expertise to build and train intelligent models. In addition, we also offer robotics consulting to help connect AI with automation systems and embedded devices. Our engineers are also skilled in digital twins technologies to help you enable simulation-driven design and testing. N-iX also has a strong community of cloud, IoT, Big Data, and other specialists to ensure data flows seamlessly between connected assets and enterprise systems.
This combination allows us to deliver AI solutions from model development and simulation to deployment, monitoring, and compliance validation.
We're proud to combine the capabilities of MATLAB AI and Simulink AI with our integration expertise to help organizations accelerate development, enhance system reliability, and maintain compliance.
By uniting MathWorks technologies with N-iX's multidisciplinary engineering experience, clients can move from concept to certified production systems faster. And do so with achieving the reliability, scalability, and trustworthiness that modern industries demand.
Final thoughts
Events like MathWorks AI Day demonstrate how AI MATLAB and MATLAB Simulink AI are redefining the boundaries of engineering innovation. AI is no longer an experimental add-on—it's becoming the foundation for safer, smarter, and more efficient systems.
MATLAB and Simulink enable engineers to build and deploy highly efficient AI models for embedded systems. It allows major gains in speed and reduced computational costs, as seen in projects like Gotion's battery estimation and Subaru's simulation optimization.
MathWorks' platform also facilitates compliance with stringent safety standards and supports advanced modeling techniques like Physics-Informed ML and Reinforcement Learning. The latter can be handy for complex, safety-critical projects within automotive and deep-tech manufacturing domains.
MATLAB and Simulink are a powerful bridge, integrating sophisticated AI/ML capabilities with established engineering workflows and open-source ecosystems. N-iX helps clients integrate MathWorks' MATLAB and Simulink tools into advanced model-based design, testing, and CI/CD processes. Let's accelerate your product development, enhance system reliability, and ensure compliance with N-iX and MathWorks by your side.
FAQ
What is MATLAB AI?
MATLAB AI refers to using MATLAB's built-in machine learning and deep learning toolboxes to develop, train, and deploy AI models in engineering, manufacturing, and scientific applications.
What is Simulink AI used for?
Simulink AI enables simulation and testing of AI algorithms in dynamic system models, allowing engineers to validate control logic and deploy AI safely to embedded hardware.
How does MATLAB Simulink AI help industries?
It bridges physics-based modeling with data-driven intelligence, supporting advanced applications in automotive, robotics, and safety-critical manufacturing systems.
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