# An attempt to generate new bridge types from latent space of denoising diffusion Implicit model

arXiv: https://arxiv.org/abs/2402.07129 · HTML: https://mlsanity.com/paper/2402.07129/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 11 Feb 2024.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 6 of 6)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [An attempt to generate new bridge types from latent space of variational autoencoder](https://mlsanity.com/paper/2311.03380/) (2023)
- [An attempt to generate new bridge types from latent space of generative adversarial network](https://mlsanity.com/paper/2401.00700/) (2024)
- [An attempt to generate new bridge types from latent space of PixelCNN](https://mlsanity.com/paper/2401.05964/) (2024)
- [An attempt to generate new bridge types from latent space of generative flow](https://mlsanity.com/paper/2401.10299/) (2024)

## Built on by (most cited first, 3 of 3)

- [Towards General Modality Translation with Contrastive and Predictive Latent Diffusion Bridge](https://mlsanity.com/paper/2510.20819/) (2025)
- [Kinematic analysis of structural mechanics based on convolutional neural network](https://mlsanity.com/paper/2405.02807/) (2024)
- [Aided design of bridge aesthetics based on Stable Diffusion fine-tuning](https://mlsanity.com/paper/2409.15812/) (2024)

## Abstract (arXiv)

Use denoising diffusion implicit model for bridge-type innovation. The process of adding noise and denoising to an image can be likened to the process of a corpse rotting and a detective restoring the scene of a victim being killed, to help beginners understand. Through an easy-to-understand algebraic method, derive the function formulas for adding noise and denoising, making it easier for beginners to master the mathematical principles of the model. Using symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge , based on Python programming language, TensorFlow and Keras deep learning platform framework , denoising diffusion implicit model is constructed and trained. From the latent space sampling, new bridge types with asymmetric structures can be generated. Denoising diffusion implicit model can organically combine different structural components on the basis of human original bridge types, and create new bridge types.