Object-Oriented Architecture: A Software Engineering-Inspired Shape Grammar for Durands Plates
- URL: http://arxiv.org/abs/2404.14448v1
- Date: Sat, 20 Apr 2024 11:51:05 GMT
- Title: Object-Oriented Architecture: A Software Engineering-Inspired Shape Grammar for Durands Plates
- Authors: Rohan Agarwal,
- Abstract summary: The focus lies on the modular generation of plates in the style of French Neoclassical architect Jean-Nicolas-Louis Durand.
The proposed methodology allows for the creation of diverse designs while adhering to the inherent logic of Durand's original plates.
- Score: 0.4532517021515834
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Addressing the challenge of modular architectural design, this study presents a novel approach through the implementation of a shape grammar system using functional and object-oriented programming principles from computer science. The focus lies on the modular generation of plates in the style of French Neoclassical architect Jean-Nicolas-Louis Durand, known for his modular rule-based method to architecture, demonstrating the system's capacity to articulate intricate architectural forms systematically. By leveraging computer programming principles, the proposed methodology allows for the creation of diverse designs while adhering to the inherent logic of Durand's original plates. The integration of Shape Machine allows a flexible framework for architects and designers, enabling the generation of complex structures in a modular fashion in existing CAD software. This research contributes to the exploration of computational tools in architectural design, offering a versatile solution for the synthesis of historically significant architectural elements.
Related papers
- On The Specialization of Neural Modules [16.83151955540625]
We study the ability of network modules to specialize to useful structures in a dataset and achieve systematic generalization.
Our results shed light on the difficulty of module specialization, what is required for modules to successfully specialize, and the necessity of modular architectures to achieve systematicity.
arXiv Detail & Related papers (2024-09-23T12:58:11Z) - Configurable Foundation Models: Building LLMs from a Modular Perspective [115.63847606634268]
A growing tendency to decompose LLMs into numerous functional modules allows for inference with part of modules and dynamic assembly of modules to tackle complex tasks.
We coin the term brick to represent each functional module, designating the modularized structure as customizable foundation models.
We present four brick-oriented operations: retrieval and routing, merging, updating, and growing.
We find that the FFN layers follow modular patterns with functional specialization of neurons and functional neuron partitions.
arXiv Detail & Related papers (2024-09-04T17:01:02Z) - Generating Daylight-driven Architectural Design via Diffusion Models [2.3020018305241337]
We present a novel daylight-driven AI-aided architectural design method.
Firstly, we formulate a method for generating massing models, producing architectural massing models using random parameters.
We integrate a daylight-driven facade design strategy, accurately determining window layouts and applying them to the massing models.
arXiv Detail & Related papers (2024-04-20T11:28:14Z) - Sketch-to-Architecture: Generative AI-aided Architectural Design [20.42779592734634]
We present a novel workflow that utilizes AI models to generate conceptual floorplans and 3D models from simple sketches.
Our work demonstrates the potential of generative AI in the architectural design process, pointing towards a new direction of computer-aided architectural design.
arXiv Detail & Related papers (2024-03-29T14:04:45Z) - Geometric Deep Learning for Computer-Aided Design: A Survey [85.79012726689511]
This survey offers a comprehensive overview of learning-based methods in computer-aided design.
It includes similarity analysis and retrieval, 2D and 3D CAD model synthesis, and CAD generation from point clouds.
It provides a complete list of benchmark datasets and their characteristics, along with open-source codes that have propelled research in this domain.
arXiv Detail & Related papers (2024-02-27T17:11:35Z) - Zero-shot Sequential Neuro-symbolic Reasoning for Automatically
Generating Architecture Schematic Designs [4.78070970632469]
This paper introduces a novel automated system for generating architecture schematic designs.
We leverage the combined strengths of generative AI (neuro reasoning) and mathematical program solvers (symbolic reasoning)
Our method can generate various building designs in accordance with the understanding of the neighborhood.
arXiv Detail & Related papers (2024-01-25T12:52:42Z) - Serving Deep Learning Model in Relational Databases [70.53282490832189]
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains.
We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks.
The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
arXiv Detail & Related papers (2023-10-07T06:01:35Z) - Machine Learning-Enabled Software and System Architecture Frameworks [48.87872564630711]
The stakeholders with data science and Machine Learning related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks.
We surveyed 61 subject matter experts from over 25 organizations in 10 countries.
arXiv Detail & Related papers (2023-08-09T21:54:34Z) - A Compositional Approach to Creating Architecture Frameworks with an
Application to Distributed AI Systems [16.690434072032176]
We show how compositional thinking can provide rules for the creation and management of architectural frameworks for complex systems.
The aim of the paper is not to provide viewpoints or architecture models specific to AI systems, but instead to provide guidelines on how a consistent framework can be built up with existing, or newly created, viewpoints.
arXiv Detail & Related papers (2022-12-27T18:05:02Z) - Dynamically Grown Generative Adversarial Networks [111.43128389995341]
We propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation.
The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator.
arXiv Detail & Related papers (2021-06-16T01:25:51Z) - Towards a Predictive Processing Implementation of the Common Model of
Cognition [79.63867412771461]
We describe an implementation of the common model of cognition grounded in neural generative coding and holographic associative memory.
The proposed system creates the groundwork for developing agents that learn continually from diverse tasks as well as model human performance at larger scales.
arXiv Detail & Related papers (2021-05-15T22:55:23Z)
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.