Douard Nicolas, Cavallucci Denis, Samet Ahmed, Giakos George
National Institute of Applied Sciences (INSA), University of Strasbourg, 24 Boulevard de la Victoire, 67000, Strasbourg, France.
Department of Electrical and Computer Engineering, Manhattan University, 3825 Corlear Ave, Riverdale, NY, 10463, USA.
Sci Rep. 2025 Aug 10;15(1):29256. doi: 10.1038/s41598-025-15067-9.
We develop an AI system that pairs engineering problems with biology-inspired solutions at a large scale, by analyzing over 101 million abstracts to identify thematic links between engineering and biology. We detect coherent themes in each domain with transformer-based embeddings and BERTopic, then link them in a topic graph that quantifies their co-occurrence. We use TRIZ (Theory of Inventive Problem Solving) analysis to show how biological principles can overcome specific engineering limitations. By integrating language models, topic modeling, and contradiction analysis, the approach highlights latent thematic overlaps. Our methodology is demonstrated in four distinct case examples-including adhesive mechanisms for robotic climbing and thermal insulation inspired by dental bonding-validating our approach. This systematic approach can accelerate the discovery of new bio-inspired innovations.
我们开发了一个人工智能系统，该系统通过分析超过1.01亿篇摘要来识别工程学与生物学之间的主题联系，从而大规模地将工程问题与受生物学启发的解决方案进行配对。我们使用基于Transformer的嵌入和BERTopic来检测每个领域中的连贯主题，然后将它们链接到一个主题图中，该图量化了它们的共现情况。我们使用TRIZ（发明问题解决理论）分析来展示生物学原理如何克服特定的工程限制。通过整合语言模型、主题建模和矛盾分析，该方法突出了潜在的主题重叠。我们的方法在四个不同的案例中得到了证明，包括机器人攀爬的粘附机制和受牙科粘结启发的隔热，验证了我们的方法。这种系统方法可以加速新的生物启发创新的发现。
