JAX FDM: A differentiable solver for inverse form-finding

TL;DR

JAX FDM is an open-source differentiable solver that enables the design of efficient 3D structures like domes and towers through inverse form-finding, integrating force density methods with gradient-based optimization.

Contribution

It introduces a novel differentiable solver combining force density methods with gradient optimization, facilitating integration with neural networks for architectural design.

Findings

Successfully designed complex 3D structures using JAX FDM

Demonstrated integration with neural network frameworks

Open-sourced the JAX FDM library for community use

Abstract

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.