Hybrid Modelling (Physics-Based + Data-Driven Approaches)

AMS provides advanced hybrid modelling solutions that combine the robustness of physics-based numerical simulation with the flexibility and scalability of data-driven artificial intelligence techniques.

This service addresses a fundamental industrial challenge: while high-fidelity simulations (e.g., finite element models) provide physical consistency and interpretability, they are often computationally expensive for real-time applications. Conversely, purely data-driven AI models may lack reliability, transparency, and generalization beyond training datasets.

AMS hybrid modelling represents an engineering implementation of Explainable Artificial Intelligence (XAI) for industrial environments. Instead of relying on black-box AI, AMS embeds physical laws, constitutive models, and mechanical principles directly into machine learning architectures. This ensures that AI predictions remain physically meaningful, interpretable, and aligned with engineering knowledge.

By constraining data-driven components with physics-based formulations, AMS delivers:

• Transparent and explainable predictive models

• Reliable digital twins with physical consistency

• Real-time industrial decision-support systems

• Improved robustness and generalization capability

• Trustworthy AI solutions suitable for safety-critical applications

This approach enables manufacturers to adopt AI solutions with confidence, ensuring performance, traceability, and compliance with industrial standards while accelerating digital transformation within Industry 4.0 ecosystems.