activity
20212024
most citedDisentangling Variational Autoencoders

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CE2024

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

Rafael Pastrana, Eder Medina, Isabel M. de Oliveira +2

Designing mechanically efficient geometry for architectural structures like shells, towers, and bridges, is an expensive iterative process. Existing techniques for solving such inv…

cs.CE2023★ 1 cited

JAX FDM: A differentiable solver for inverse form-finding

Rafael Pastrana, Deniz Oktay, Ryan P. Adams +1

We introduce JAX FDM, a differentiable solver to design mechanically efficient shapes for 3D structures conditioned on target architectural, fabrication and structural properties.…

cs.LG2022★ 1 cited

Disentangling Variational Autoencoders

Rafael Pastrana

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, la…

cs.CE2021

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

Rafael Pastrana, Patrick Ole Ohlbrock, Thomas Oberbichler +2

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…

cs.RO2021

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

Edvard P. G. Bruun, Rafael Pastrana, Vittorio Paris +4

Geometrically complex masonry structures (e.g., arches, domes, vaults) are traditionally built with expensive scaffolding or falsework to provide stability during construction. The…