Advanced technologies | Engineering
Issue 02 - March 2026
Cutting-edge technologies in engineering: Simulation, FEM analysis, holistic lightweight construction and technical weight management.
Introduction
Artificial intelligence is no longer a topic of the future that is limited to innovation labs and strategy papers. It is already changing our approach to simulation, computer-aided engineering (CAE) and product development.
In our latest roundup, we examine the most relevant AI technologies for engineering companies and analyze where measurable business benefits are emerging and where key research and implementation gaps remain.
The following overview summarizes the most important findings for 2025 and 2026. The focus is clear: prioritization, practicability and concrete instructions for action.
AI as a force amplifier for physics-based simulations
The most significant advances are taking place at the interface between high-performance computing and artificial intelligence. Instead of replacing established engineering methods, AI is expanding their possibilities and significantly increasing their efficiency.
1. replacement models - from hours to seconds
Substitute models use machine learning to approximate computationally intensive simulations such as finite element analysis (FEA) or computational fluid dynamics (CFD). Instead of performing complete, high-precision simulations for each design iteration, engineers can rely on trained models that provide fast and highly accurate predictions.
The advantages are considerable:
- Acceleration of simulation runtimes by factors of 100 to 1000
- Exploring design spaces that were previously impractical
- Reduced dependence on expensive high-performance computing resources
- Shorter development cycles and faster time to market
At the same time, caution is advised. The predictive power of surrogate models is closely related to their training range. Outside this range, performance can decrease significantly. Robust validation procedures and clearly defined confidence limits are therefore essential for industrial use.
2. physics-based neural networks - embedding physics in AI
Physics-based neural networks (PINNs) integrate the basic physical equations directly into the training processes of neural networks. By embedding physical constraints into the loss function, these models aim to combine data-driven learning with established scientific principles.
The potential is considerable:
- Reduced dependency on large training data sets
- Ability to solve complex partial differential equations
- Promising applications in multiphysical environments
However, PINNs are still largely in the research and pilot phase. Training stability, computational scalability and the application to realistic three-dimensional geometries continue to pose challenges that need to be overcome before widespread industrial application.
3. generative design - AI as a creative co-engineer
Generative design has left the experimental phase behind and entered industrial practice, particularly in automotive, aerospace and additive manufacturing.
Its value lies in its ability:
- Achieving radical lightweight structures
- Create non-intuitive, yet powerful geometries
- Manufacturing constraints should be considered from the earliest design stages.
In highly competitive markets, generative design is no longer a novelty. It is increasingly becoming a differentiating feature.
Digital twins as a strategic backbone
Digital twins integrate simulation models, sensor data and operating systems into a coherent digital representation of physical systems. Their strategic importance is constantly increasing.
According to market forecasts, the global market for digital twins could grow by around 163 billion US dollars by 2029, with average annual growth rates of almost 65%.
The effects on engineering and operation can be considerable.
- Reduction of unplanned downtime by up to 45 percent
- Advanced strategies for predictive maintenance
- Virtual commissioning and process validation
- Earlier integration of simulations into the conceptual design phases
At the same time, implementation remains complex. Data harmonization, system interoperability and organizational coordination are decisive success factors.
Technology Maturity and ImpactTechnology Maturity and Impact
The management summary provides a structured assessment of technological maturity and industrial impact.
Technologies with a high degree of maturity and direct business relevance include
- Generative design
- Digital twins
- AI-supported optimization
- Predictive maintenance
Areas with high long-term potential but a continuing need for research include:
- Multiphysical AI coupling
- Explainable AI
- Federated learning
- Data-efficient learning
Strategic planning requires a balance between short-term value creation and long-term competence development.
Market signals for 2025
Several developments point to a significant change in the engineering landscape:
- Native AI integration in CAE platforms
- Generative AI directly integrated into simulation processes
- Technical co-pilots support model creation and validation
- Active learning approaches reduce the testing and validation effort by up to 70 percent.
- Increasing convergence of AI architectures and high-performance computing
Taken together, these signals point to the emergence of AI-native development environments.
Main challenges
Despite the strong momentum, important obstacles remain:
- Generalization and robustness: Many AI models only work reliably within the limits of their training data.
- Data scarcity: High-quality data sets are still limited, especially in multiphysical and nonlinear applications.
- Toolchain integration: Standardized interfaces between AI systems and established CAE tools are often lacking.
- Physical plausibility: Compliance with conservation laws and the guarantee of physical consistency are essential.
- Governance and validation: The implementation of AI requires structured quality assurance, version control and cross-functional monitoring.
Addressing these issues is of fundamental importance for the responsible and sustainable expansion of AI in engineering.
Practical recommendations for CAE teams
Based on our analysis, several concrete steps can accelerate the successful implementation:
- Start with an effective pilot project that focuses on recurring simulation bottlenecks and measurable performance indicators.
- Invest in a robust data infrastructure, including structured simulation archives, centralized data storage and MLOps frameworks.
- Combine surrogate models with multi-fidelity workflows, using AI for fast screening and high-resolution FEM for final validation.
- Apply active learning techniques to prioritize targeted simulations instead of comprehensive parameter studies.
- Clear governance structures for AI should be established, including validation protocols, version control and defined responsibilities.
- Close the feedback loop by continuously updating the models with operating data from digital twin environments.
AI-controlled CAE workflow
A future-oriented engineering workflow increasingly follows a closed loop structure:
AI-controlled CAE workflow - Closed-loop control structure (Copyright: TGM)
Such a framework shortens the time to market and at the same time expands design diversity and innovation potential.
Concluding remarks
Artificial intelligence does not replace traditional simulation methods. It strengthens and expands them.
The future of engineering lies in hybrid ecosystems that combine FEM, surrogate modeling, digital twins and active learning in a coherent strategic framework.
The key question is no longer whether AI will transform computer-aided engineering (CAE). Rather, the decisive factor is how structured, disciplined and future-oriented this transformation will be in your company.
I look forward to your assessment.
Where do you currently see the greatest impact of AI in engineering?
What pilot projects are currently running in your organization?
What obstacles are slowing down implementation?
Let's shape the next phase of technological innovation together.
Best regards,
Hans-Peter Dahm
References
Guo, X., Li, W., & Iorio, F. – “Neural Networks for Fluid Flow Prediction” (2021). Demonstrates how deep learning greatly speeds up CFD & simulation tasks. Cite: DOI:10.1016/j.jcp.2021.109638
Bongini et al. – “Machine Learning Surrogate Models in Engineering Design” (2022). High accuracy surrogate models for design space exploration. Cite: Engineering Applications of AI, Vol. 104
Raissi, M., Perdikaris, P., & Karniadakis, G.E. – “Physics-Informed Neural Networks” (Journal of Computational Physics, 2019). Introduced PINNs for solving PDEs using physics constraints. Cite: J. Comput. Phys. https://doi.org/10.1016/j.jcp.2018.10.045
Karniadakis et al. – “Physics-informed Machine Learning” (Nature Reviews Physics, 2021). PINN challenges & future outlook. Cite: Nat Rev Phys 3, 422-440
Autodesk (2023 Report) - Evolution of generative design in industrial workflows. Cite: Autodesk White Paper (https://dam.autodesk.com/autodesk/GD_Industrial_Study.pdf)
Cao et al. – “Feature-Based Generative Design Using AI” (2022). Practical impact on product lightweighting. Cite: Journal of Mechanical Design
Gartner Digital Twin Research - Digital twin market growth estimates. Cite: Gartner Market Guide 2025
IBM Research - Trends in digital twin adoption & predictive analytics. Cite: IBM Institute for Business Value, 2024
Settles, B. — “Active Learning Literature Survey” (2010). Foundational review for AI sampling approaches. Cite: University of Wisconsin-Madison Technical Report
Foster + Partners + NVIDIA Pilot Study - Active Learning in design simulation. Cite: NVIDIA Technical Brief 2024
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