# Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers

arXiv: https://arxiv.org/abs/2212.13196 · HTML: https://mlsanity.com/paper/2212.13196/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 26 Dec 2022.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Deep Generative Models in Engineering Design: A Review](https://mlsanity.com/paper/2110.10863/) (2021)
- [Re-evaluating Automatic Metrics for Image Captioning](https://mlsanity.com/paper/1612.07600/) (2016)
- [Data-Driven Design-by-Analogy: State of the Art and Future Directions](https://mlsanity.com/paper/2106.01592/) (2021)
- [Engineering Knowledge Graph from Patent Database](https://mlsanity.com/paper/2106.06739/) (2021)
- [Generative Pre-Trained Transformer for Design Concept Generation: An Exploration](https://mlsanity.com/paper/2111.08489/) (2021)
- [Natural Language Processing in-and-for Design Research](https://mlsanity.com/paper/2111.13827/) (2021)
- [Data-Driven Innovation: What Is It](https://mlsanity.com/paper/2201.08184/) (2022)
- [Guiding Data-Driven Design Ideation by Knowledge Distance](https://mlsanity.com/paper/2210.10104/) (2022)
- [Generative Design Ideation: A Natural Language Generation Approach](https://mlsanity.com/paper/2204.09658/) (2022)
- [Intelligent Amphibious Ground-Aerial Vehicles: State of the Art Technology for Future Transportation](https://mlsanity.com/paper/2207.11384/) (2022)

## Built on by (most cited first, 13 of 13)

- [Multi-modal Machine Learning in Engineering Design: A Review and Future Directions](https://mlsanity.com/paper/2302.10909/) (2023)
- [Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativity](https://mlsanity.com/paper/2302.12832/) (2023)
- [Conceptual Design Generation Using Large Language Models](https://mlsanity.com/paper/2306.01779/) (2023)
- [AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Models](https://mlsanity.com/paper/2403.13002/) (2024)
- [Toward Artificial Empathy for Human-Centered Design: A Framework](https://mlsanity.com/paper/2303.10583/) (2023)
- [Opportunities for Large Language Models and Discourse in Engineering Design](https://mlsanity.com/paper/2306.09169/) (2023)
- [Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning](https://mlsanity.com/paper/2308.10454/) (2023)
- [Inspired by AI? A Novel Generative AI System To Assist Conceptual Automotive Design](https://mlsanity.com/paper/2407.11991/) (2024)
- [The Innovation Paradox: Concept Space Expansion with Diminishing Originality and the Promise of Creative AI](https://mlsanity.com/paper/2303.13300/) (2023)
- [Exploring the Capabilities of Large Language Models for Generating Diverse Design Solutions](https://mlsanity.com/paper/2405.02345/) (2024)
- [Human-in-the-Loop: Quantitative Evaluation of 3D Models Generation by Large Language Models](https://mlsanity.com/paper/2509.07010/) (2025)
- [Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models](https://mlsanity.com/paper/2406.11934/) (2024)
- [Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era](https://mlsanity.com/paper/2506.09755/) (2025)

## Abstract (arXiv)

Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a specific form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a data-driven approach to bridge the gap. This paper proposes a generative design approach based on the generative pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely GPT-3, is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the mapping relevancy between the domains within the generated BID concepts. The approach is evaluated and then employed in a real-world project of designing light-weighted flying cars during its conceptual design phase The results show our approach can generate BID concepts with good performance.