Gonzalo Casas


Last Name

Casas

First Name

Gonzalo

ORCID

0000-0002-2061-1533

Organisational unit

03709 - Kohler, Matthias / Kohler, Matthias

Filters

2023 - 20257
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Publications 1 - 7 of 7
  • Introduction to COMPAS framework
    Item type: Presentation
    Casas, Gonzalo (2023)
  • Open Architecture
    Item type: Presentation
    Casas, Gonzalo (2023)
  • ARA - Grasshopper Plugin for AI-Augmented Inverse Design
    Item type: Conference Paper
    Apolinarska, Aleksandra Anna; Casas, Gonzalo; Salamanca Miño, Luis; et al. (2024)
    Scalable Disruptors
    We introduce ARA - a Grasshopper plugin that integrates AI-augmented and data-driven design with parametric modelling, enabling architects, engineers and designers to efficiently generate design solutions with the assistance of generative neural networks. The inverse design paradigm accelerates design exploration by providing many different design variants that match requested objectives. In this paper, we present the main features of ARA and demonstrate its application in two design scenarios. The presented workflow allows to create project-specific solutions, which includes generation of the dataset, training and deployment of a custom autoencoder model to generate designs, and various visualisation tools for data analysis and performance evaluation. We also briefly explain the underlying machine learning methods and provide some commonly used best practices. The main motivation for this work is to lower the barrier to entry for users without machine-learning background and to accelerate adoption of deep-learning methods for everyday design tasks.
  • Robotic Fabrication with COMPAS
    Item type: Presentation
    Casas, Gonzalo (2023)
  • Cooperative augmented assembly (CAA): augmented reality for on-site cooperative robotic fabrication
    Item type: Journal Article
    Alexi, Eleni Vasiliki; Kenny, Joseph Clair; Atanasova, Lidia; et al. (2024)
    Construction Robotics
    Recent years have witnessed significant advances in computational design and robotic fabrication for large-scale manufacturing. Although these advances have enhanced the speed, precision, and reproducibility of digital fabrication processes, they often lack adaptability and fail to integrate manual actions in a digital model. Addressing this challenge, the present study introduces cooperative augmented assembly (CAA), a phone-based mobile Augmented Reality (AR) application that facilitates cooperative assembly of complex timber structures between humans and robots. CAA enables augmented manual assembly, intuitive robot control and supervision, and task sharing between humans and robots, creating an adaptive digital fabrication process. To allocate tasks to manual or robotic actions, the mobile AR application allows multiple users to access a shared digital workspace. This is achieved through a flexible communication system that allows numerous users and robots to cooperate seamlessly. By harnessing a cloud-based augmented reality system in combination with an adaptive digital model, CAA aims to better incorporate human actions in robotic fabrication setups, facilitating human–machine cooperation workflows and establishing a highly intuitive, adaptable digital fabrication process within the Architecture, Engineering, and Construction sector.
  • Fabricación robótica con COMPAS framework
    Item type: Presentation
    Casas, Gonzalo (2023)
  • AIXD: AI-eXtended Design Toolbox for data-driven and inverse design
    Item type: Journal Article
    Maissen, Alessandro; Apolinarska, Aleksandra Anna; Kuhn, Sophia V.; et al. (2025)
    Computer Aided Design
    Design processes, in many disciplines like architecture, civil engineering or mechanical engineering, involve navigating large, high-dimensional and heterogeneous data. While AI-driven approaches like inverse design and surrogate modeling can enhance design exploration, their adoption is hindered by complex workflows and the need for coding and machine learning expertise. To address this, we introduce AI-eXtended Design (AIXD): a low-code, open-source toolbox that integrates AI into computational design. AIXD simplifies handling of mixed data types, as well as the analysis, training, and deployment of machine learning models for inverse design, surrogate modeling, and sensitivity analysis, enabling domain experts to rapidly explore diverse solutions with minimal coding. In this paper, we show the functionalities of the toolbox, and we demonstrate AIXD’s capabilities in architectural and engineering design applications, showing how it accelerates performance evaluation, generates high-performing alternatives, and improves design understanding by delivering new insights. By bridging AI and design practice, AIXD lowers the entry barrier to data-driven methods, making AI-extended design more accessible and efficient.
Publications 1 - 7 of 7