Cutting-edge technologies in engineering: Simulation, FEM analysis, holistic lightweight construction and technical weight management.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n The following overview summarizes the most important findings for 2025 and 2026. The focus is clear: prioritization, practicability and concrete instructions for action.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n The advantages are considerable:<\/p>\n\n\n\n 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.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n The potential is considerable:<\/p>\n\n\n\n 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.<\/p>\n\n\n\n Generative design has left the experimental phase behind and entered industrial practice, particularly in automotive, aerospace and additive manufacturing.<\/p>\n\n\n\n Its value lies in its ability:<\/p>\n\n\n\n In highly competitive markets, generative design is no longer a novelty. It is increasingly becoming a differentiating feature.<\/p>\n\n\n\n Digital twins integrate simulation models, sensor data and operating systems into a coherent digital representation of physical systems. Their strategic importance is constantly increasing.<\/p>\n\n\n\n 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%.<\/p>\n\n\n\n The effects on engineering and operation can be considerable.<\/p>\n\n\n\n At the same time, implementation remains complex. Data harmonization, system interoperability and organizational coordination are decisive success factors.<\/p>\n\n\n\n The management summary provides a structured assessment of technological maturity and industrial impact.<\/p>\n\n\n\n Technologies with a high degree of maturity and direct business relevance include<\/p>\n\n\n\n Areas with high long-term potential but a continuing need for research include:<\/p>\n\n\n\n Strategic planning requires a balance between short-term value creation and long-term competence development.<\/p>\n\n\n\n Several developments point to a significant change in the engineering landscape:<\/p>\n\n\n\n Taken together, these signals point to the emergence of AI-native development environments.<\/p>\n\n\n\n Despite the strong momentum, important obstacles remain:<\/p>\n\n\n\n Addressing these issues is of fundamental importance for the responsible and sustainable expansion of AI in engineering.<\/p>\n\n\n\n Based on our analysis, several concrete steps can accelerate the successful implementation:<\/p>\n\n\n\n A future-oriented engineering workflow increasingly follows a closed loop structure:<\/p>\n\n\n\n AI-controlled CAE workflow - Closed-loop control structure (Copyright: TGM)<\/span><\/p>\n\n\n\n Such a framework shortens the time to market and at the same time expands design diversity and innovation potential.<\/p>\n\n\n\n Artificial intelligence does not replace traditional simulation methods. It strengthens and expands them.<\/p>\n\n\n\n The future of engineering lies in hybrid ecosystems that combine FEM, surrogate modeling, digital twins and active learning in a coherent strategic framework.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n I look forward to your assessment.<\/p>\n\n\n\n Where do you currently see the greatest impact of AI in engineering?<\/strong><\/p>\n<\/blockquote>\n\n\n\n What pilot projects are currently running in your organization?<\/strong><\/p>\n<\/blockquote>\n\n\n\n What obstacles are slowing down implementation?<\/strong><\/p>\n<\/blockquote>\n\n\n\n Let's shape the next phase of technological innovation together.<\/p>\n\n\n\n <\/p>\n\n\n\n Best regards,<\/p>\n\n\n\n Hans-Peter Dahm<\/p>\n\n\n\n References<\/strong><\/p>\n\n\n\n Guo, X., Li, W., & Iorio, F.<\/em> \u2013 \u201cNeural Networks for Fluid Flow Prediction\u201d (2021).<\/em> Demonstrates how deep learning greatly speeds up CFD & simulation tasks. Cite: DOI:10.1016\/j.jcp.2021.109638<\/p>\n\n\n\n Bongini et al.<\/em> \u2013 \u201cMachine Learning Surrogate Models in Engineering Design\u201d (2022).<\/em> High accuracy surrogate models for design space exploration. Cite: Engineering Applications of AI, Vol. 104<\/p>\n\n\n\n Raissi, M., Perdikaris, P., & Karniadakis, G.E.<\/em> \u2013 \u201cPhysics-Informed Neural Networks\u201d (Journal of Computational Physics, 2019).<\/em> Introduced PINNs for solving PDEs using physics constraints. Cite: J. Comput. Phys. https:\/\/doi.org\/10.1016\/j.jcp.2018.10.045<\/a><\/p>\n\n\n\n![\"\"](\"https:\/\/www.tgm.solutions\/wp-content\/uploads\/1772479084007.png\")
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\n\n\n\nIntroduction<\/h2>\n\n\n\n
\n\n\n\nAI as a force amplifier for physics-based simulations<\/h2>\n\n\n\n
1. replacement models - from hours to seconds<\/h2>\n\n\n\n
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2. physics-based neural networks - embedding physics in AI<\/h2>\n\n\n\n
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3. generative design - AI as a creative co-engineer<\/h2>\n\n\n\n
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\n\n\n\nDigital twins as a strategic backbone<\/h2>\n\n\n\n
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\n\n\n\nTechnology Maturity and ImpactTechnology Maturity and Impact<\/h2>\n\n\n\n
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\n\n\n\nMarket signals for 2025<\/h2>\n\n\n\n
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\n\n\n\nMain challenges<\/h2>\n\n\n\n
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\n\n\n\nPractical recommendations for CAE teams<\/h2>\n\n\n\n
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\n\n\n\nAI-controlled CAE workflow<\/h2>\n\n\n\n
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\n\n\n\nConcluding remarks<\/h2>\n\n\n\n
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