Top technologies in engineering: simulation, FEM analysis, holistic lightweight construction and technical weight management.<\/p>\n\n\n\n Artificial intelligence is no longer a forward-looking concept confined to innovation labs and strategy decks. It is already transforming how we approach simulation, computer-aided engineering (CAE), and product development.<\/p>\n\n\n\n In our latest Executive Summary, we examine the most relevant AI technologies for engineering organizations and assess where measurable business value is emerging, as well as where important research and implementation gaps remain.<\/p>\n\n\n\n The following overview summarizes the key insights for 2025 and 2026. The focus is clear: prioritization, practicality, and actionable direction.<\/p>\n\n\n\n The most significant advances are taking place at the intersection of high-performance computing and artificial intelligence. Rather than replacing established engineering methods, AI is extending their capabilities and dramatically increasing their efficiency.<\/p>\n\n\n\n Surrogate models<\/a> use machine learning to approximate computationally expensive simulations such as finite element analysis (FEM) or computational fluid dynamics (CFD). Instead of running full high-fidelity simulations for every design iteration, engineers can rely on trained models that deliver rapid predictions with high accuracy.<\/p>\n\n\n\n The benefits are substantial:<\/p>\n\n\n\n At the same time, caution is required. The predictive strength of surrogate models is closely tied to their training domain. Outside that domain, performance may decline significantly. Robust validation procedures and clearly defined trust boundaries are therefore essential for industrial deployment.<\/p>\n\n\n\n Physics-Informed Neural Networks<\/a> (PINNs) integrate governing physical equations directly into neural network training processes. 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 remain largely in research and pilot phases. Training stability, computational scalability, and application to realistic three-dimensional geometries continue to present challenges that must be addressed before widespread industrial adoption.<\/p>\n\n\n\n Generative design<\/a> has moved beyond experimentation and into industrial practice, particularly in automotive, aerospace, and additive manufacturing environments.<\/p>\n\n\n\n Its value lies in the ability to:<\/p>\n\n\n\n In competitive markets, generative design is no longer a novelty. It is increasingly a differentiating capability.<\/p>\n\n\n\n Digital twins<\/a> integrate simulation models, sensor data, and operational systems into a coherent digital representation of physical assets. Their strategic importance continues to grow.<\/p>\n\n\n\n Market projections suggest that the global digital twin market could expand by approximately USD 163 billion by 2029, with compound annual growth rates close to 65 percent.<\/p>\n\n\n\n The impact on engineering and operations can be significant:<\/p>\n\n\n\n At the same time, implementation remains complex. Data harmonization, system interoperability, and organizational alignment are critical success factors.<\/p>\n\n\n\n The Executive Summary provides a structured assessment of technological maturity and industrial impact.<\/p>\n\n\n\n Technologies with high maturity and immediate business relevance include:<\/p>\n\n\n\n Areas with strong long-term potential but continued research demand include:<\/p>\n\n\n\n Strategic planning requires balancing short-term value creation with long-term capability building.<\/p>\n\n\n\n Several developments indicate a clear shift in the engineering landscape:<\/p>\n\n\n\n Taken together, these signals point towards the emergence of AI-native engineering environments.<\/p>\n\n\n\n Despite strong momentum, important obstacles remain:<\/p>\n\n\n\n Addressing these issues is fundamental to scaling AI in engineering responsibly and sustainably.<\/p>\n\n\n\n Based on our analysis, several concrete steps can accelerate successful adoption:<\/p>\n\n\n\n A future-oriented engineering workflow increasingly follows a closed-loop structure:<\/p>\n\n\n\n AI Driven CAE Workflow - Closed Loop Structure (Copyright: TGM)<\/span><\/p>\n\n\n\n Such a framework reduces time-to-market while expanding design diversity and innovation potential.<\/p>\n\n\n\n Artificial intelligence does not replace classical simulation methods. It strengthens and extends them.<\/p>\n\n\n\n The future of engineering lies in hybrid ecosystems that combine FEM, surrogate modeling, digital twins, and active learning within a coherent strategic framework.<\/p>\n\n\n\n The central question is no longer whether AI will transform CAE. The decisive question is how structured, disciplined, and forward-looking that transformation will be within your organization.<\/p>\n\n\n\n I look forward to your perspective:<\/p>\n\n\n\n Where do you currently observe the greatest impact of AI in engineering?<\/strong><\/p>\n<\/blockquote>\n\n\n\n Which pilot initiatives are underway in your organization?<\/strong><\/p>\n<\/blockquote>\n\n\n\n What barriers are slowing implementation?<\/strong><\/p>\n<\/blockquote>\n\n\n\n Let us shape the next phase of engineering 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\")
<\/figure>\n\n\n\n
\n\n\n\nIntroduction<\/h2>\n\n\n\n
\n\n\n\nAI as a Force Multiplier for Physics-Based Simulation<\/h2>\n\n\n\n
1 Surrogate Models - From Hours to Seconds<\/h2>\n\n\n\n
\n
2 Physics-Informed Neural Networks - Embedding Physics into AI<\/h2>\n\n\n\n
\n
3. generative design - AI as a creative co-engineer<\/h2>\n\n\n\n
\n
\n\n\n\nDigital Twins as the Strategic Backbone<\/h2>\n\n\n\n
\n
\n\n\n\nTechnology Maturity and Impact<\/h2>\n\n\n\n
\n
\n
\n\n\n\nMarket Signals for 2025<\/h2>\n\n\n\n
\n
\n\n\n\nKey Challenges<\/h2>\n\n\n\n
\n
\n\n\n\nPractical Recommendations for CAE Teams<\/h2>\n\n\n\n
\n
\n\n\n\nAI-Driven CAE Workflow<\/h2>\n\n\n\n
![\"\"](\"https:\/\/www.tgm.solutions\/wp-content\/uploads\/1772480290411.png\")
<\/figure>\n\n\n\n
\n\n\n\nFinal Reflections<\/h2>\n\n\n\n
\n
\n
\n
\n\n\n\n