Automating Computational Design with Generative AI

• URL: http://arxiv.org/abs/2307.02511v2
• Date: Fri, 3 May 2024 11:08:17 GMT
• Title: Automating Computational Design with Generative AI
• Authors: Joern Ploennigs, Markus Berger,
• Abstract summary: We explain how the underlying diffusion-models work and propose novel refinement approaches to improve semantic encoding and generation quality. In several experiments we show that we can improve validity of generated floor plans from 6% to 90%.
• Score: 0.5919433278490629
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI image generators based on diffusion models have recently garnered attention for their capability to create images from simple text prompts. However, for practical use in civil engineering they need to be able to create specific construction plans for given constraints. This paper investigates the potential of current AI generators in addressing such challenges, specifically for the creation of simple floor plans. We explain how the underlying diffusion-models work and propose novel refinement approaches to improve semantic encoding and generation quality. In several experiments we show that we can improve validity of generated floor plans from 6% to 90%. Based on these results we derive future research challenges considering building information modelling. With this we provide: (i) evaluation of current generative AIs; (ii) propose improved refinement approaches; (iii) evaluate them on various examples; (iv) derive future directions for diffusion models in civil engineering.

Related papers

• Boosting Alignment for Post-Unlearning Text-to-Image Generative Models [55.82190434534429]
  Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data. This often inadvertently leads to the generation of harmful or inappropriate content and raises copyright concerns. We propose a framework that seeks an optimal model update at each unlearning iteration, ensuring monotonic improvement on both objectives.
  arXiv  Detail & Related papers  (2024-12-09T21:36:10Z)
• AI-Aided Kalman Filters [65.35350122917914]
  The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. Recent developments illustrate the possibility of fusing deep neural networks (DNNs) with classic Kalman-type filtering. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms.
  arXiv  Detail & Related papers  (2024-10-16T06:47:53Z)
• Brain-inspired Artificial Intelligence: A Comprehensive Review [15.964784631512414]
  Review explores the diverse design inspirations that have shaped modern AI models, i.e., brain-inspired artificial intelligence (BIAI) We present a classification framework that categorizes BIAI approaches into physical structure-inspired and human behavior-inspired models. We examine the real-world applications where different BIAI models excel, highlighting their practical benefits and deployment challenges.
  arXiv  Detail & Related papers  (2024-08-27T06:49:50Z)
• Learning to Plan and Generate Text with Citations [69.56850173097116]
  We explore the attribution capabilities of plan-based models which have been recently shown to improve the faithfulness, grounding, and controllability of generated text. We propose two attribution models that utilize different variants of blueprints, an abstractive model where questions are generated from scratch, and an extractive model where questions are copied from the input.
  arXiv  Detail & Related papers  (2024-04-04T11:27:54Z)
• Generative AI Models for Different Steps in Architectural Design: A Literature Review [14.910709576423576]
  It is essential to comprehend the principles and advancements of generative AI models and analyze their relevance in architecture applications. This paper first provides an overview of generative AI technologies, with a focus on probabilistic diffusion models (DDPMs), 3D generative models, and foundation models. We subdivide the architectural design process into six steps and review related research projects in each step from 2020 to the present.
  arXiv  Detail & Related papers  (2024-03-30T13:25:11Z)
• Generative AI in Vision: A Survey on Models, Metrics and Applications [0.0]
  Generative AI models have revolutionized various fields by enabling the creation of realistic and diverse data samples. Among these models, diffusion models have emerged as a powerful approach for generating high-quality images, text, and audio. This survey paper provides a comprehensive overview of generative AI diffusion and legacy models, focusing on their underlying techniques, applications across different domains, and their challenges.
  arXiv  Detail & Related papers  (2024-02-26T07:47:12Z)
• Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians [3.069335774032178]
  Diffusion models offer state of the art performance in generative AI for images. We provide a brief introduction to diffusion models for applied mathematicians and statisticians.
  arXiv  Detail & Related papers  (2023-12-21T20:20:52Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model [93.8067369210696]
  Text-to-image generation (TTI) refers to the usage of models that could process text input and generate high fidelity images based on text descriptions. Diffusion models are one prominent type of generative model used for the generation of images through the systematic introduction of noises with repeating steps. In the era of large models, scaling up model size and the integration with large language models have further improved the performance of TTI models.
  arXiv  Detail & Related papers  (2023-09-02T03:27:20Z)
• Conditional Generation with a Question-Answering Blueprint [84.95981645040281]
  We advocate planning as a useful intermediate representation for rendering conditional generation less opaque and more grounded. We obtain blueprints automatically by exploiting state-of-the-art question generation technology. We develop Transformer-based models, each varying in how they incorporate the blueprint in the generated output.
  arXiv  Detail & Related papers  (2022-07-01T13:10:19Z)
• A survey on Variational Autoencoders from a GreenAI perspective [0.0]
  Variational AutoEncoders (VAEs) are powerful generative models that merge elements from statistics and information theory with the flexibility offered by deep neural networks. This article provides a comparative evaluation of some of the most successful, recent variations of VAEs.
  arXiv  Detail & Related papers  (2021-03-01T15:26:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.