JAX FDM: A differentiable solver for inverse form-finding

Rafael Pastrana, Deniz Oktay, Ryan P Adams, Sigrid Adriaenssens

Keywords: Structural design, Structural optimization, Differentiable physics, Automatic differentiation, Force density method, Form finding, JAX

TL;DR: We propose JAX FDM, an open-source, differentiable solver that facilitates the design of mechanically efficient 3D structures conditioned on additional target properties

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

Submission Number: 11