arXiv subjects

Rafael Pastrana

Publications and source records attributed to Rafael Pastrana.

5 recordsLinked to original sources

arXiv2025

Real-time design of architectural structures with differentiable mechanics and neural networks

Designing mechanically efficient geometry for architectural structures like shells, towers, and bridges, is an expensive iterative process. Existing techniques for solving such inverse problems rely on traditional optimization methods, which are slow and computationally expensive, limiting iteration speed and design exploration. Neural networks would seem to offer a solution via data-driven amortized optimization, but they often require extensive fine-tuning and cannot ensure that important design criteria, such as mechanical integrity, are met. In this work, we combine neural networks with a differentiable mechanics simulator to develop a model that accelerates the solution of shape approximation problems for architectural structures represented as bar systems. This model explicitly guarantees compliance with mechanical constraints while generating designs that closely match target geometries. We validate our approach in two tasks, the design of masonry shells and cable-net towers. Our model achieves better accuracy and generalization than fully neural alternatives, and comparable accuracy to direct optimization but in real time, enabling fast and reliable design exploration. We further demonstrate its advantages by integrating it into 3D modeling software and fabricating a physical prototype. Our work opens up new opportunities for accelerated mechanical design enhanced by neural networks for the built environment.

cs.CE↗

arXiv2023

JAX FDM: A differentiable solver for inverse form-finding

We introduce JAX FDM, a differentiable solver to design mechanically efficient shapes for 3D structures conditioned on target architectural, fabrication and structural properties. Examples of such structures are domes, cable nets and towers. JAX FDM solves these inverse form-finding problems by combining the force density method, differentiable sparsity and gradient-based optimization. Our solver can be paired with other libraries in the JAX ecosystem to facilitate the integration of form-finding simulations with neural networks. We showcase the features of JAX FDM with two design examples. JAX FDM is available as an open-source library at https://github.com/arpastrana/jax_fdm.

cs.CE↗

arXiv2022

Disentangling Variational Autoencoders

A variational autoencoder (VAE) is a probabilistic machine learning framework for posterior inference that projects an input set of high-dimensional data to a lower-dimensional, latent space. The latent space learned with a VAE offers exciting opportunities to develop new data-driven design processes in creative disciplines, in particular, to automate the generation of multiple novel designs that are aesthetically reminiscent of the input data but that were unseen during training. However, the learned latent space is typically disorganized and entangled: traversing the latent space along a single dimension does not result in changes to single visual attributes of the data. The lack of latent structure impedes designers from deliberately controlling the visual attributes of new designs generated from the latent space. This paper presents an experimental study that investigates latent space disentanglement. We implement three different VAE models from the literature and train them on a publicly available dataset of 60,000 images of hand-written digits. We perform a sensitivity analysis to find a small number of latent dimensions necessary to maximize a lower bound to the log marginal likelihood of the data. Furthermore, we investigate the trade-offs between the quality of the reconstruction of the decoded images and the level of disentanglement of the latent space. We are able to automatically align three latent dimensions with three interpretable visual properties of the digits: line weight, tilt and width. Our experiments suggest that i) increasing the contribution of the Kullback-Leibler divergence between the prior over the latents and the variational distribution to the evidence lower bound, and ii) conditioning input image class enhances the learning of a disentangled latent space with a VAE.

cs.LG↗

arXiv2022

Constrained Form-Finding of Tension-Compression Structures using Automatic Differentiation

This paper proposes a computational approach to form-find pin-jointed, bar structures subjected to combinations of tension and compression forces. The generated equilibrium states can meet force and geometric constraints via gradient-based optimization. We achieve this by extending the combinatorial equilibrium modeling (CEM) framework in three important ways. First, we introduce a new topological object, the auxiliary trail, to expand the range of structures that can be form-found with the framework. Then, we leverage automatic differentiation (AD) to obtain an exact value of the gradient of the sequential and iterative calculations of the CEM form-finding algorithm, instead of a numerical approximation. Finally, we encapsulate our research developments into an open-source design tool written in Python that is usable across different CAD platforms and operating systems. After studying four different structures -- a self-stressed planar tensegrity, a tree canopy, a curved bridge, and a spiral staircase -- we demonstrate that our approach enables the solution of constrained form-finding problems on a diverse range of structures more efficiently than in previous work.

cs.CE↗

arXiv2021

Three Cooperative Robotic Fabrication Methods for the Scaffold-Free Construction of a Masonry Arch

Geometrically complex masonry structures (e.g., arches, domes, vaults) are traditionally built with expensive scaffolding or falsework to provide stability during construction. The process of building such structures can potentially be improved through the use of multiple robots working together in a cooperative assembly framework. Here a robot is envisioned as both a placement and external support agent during fabrication - the unfinished structure is supported in such a way that scaffolding is not required. The goal of this paper is to present and validate the efficacy of three cooperative fabrication approaches using two or three robots, for the scaffold-free construction of a stable masonry arch from which a medium-span vault is built. A simplified numerical method to represent a masonry structure is first presented and validated to analyse systems composed of discrete volumetric elements. This method is then used to evaluate the effect of the three cooperative robotic fabrication strategies on the stability performance of the central arch. The sequential method and cantilever method, which utilize two robotic arms, are shown to be viable methods, but have challenges related to scalability and robustness. By adding a third robotic agent, it becomes possible to determine a structurally optimal fabrication sequence through a multi-objective optimization process. The optimized three robot method is shown to significantly improve the structural behavior over all fabrication steps. The modeling approaches presented in this paper are broadly formulated and widely applicable for the analysis of cooperative robotic fabrication sequences for the construction of discrete element structures across scales and materials.

cs.RO↗