JAX FDM: A differentiable solver for inverse form-finding

Pastrana, R., Oktay, D., Adams, R. P., & Adriaenssens, S. (2023). JAX FDM: A differentiable solver for inverse form-finding. ArXiv Preprint ArXiv:2307.12407.

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 this URL: https://github.com/arpastrana/jax_fdm.

  @article{pastrana2023jax,
  year = {2023},
  title = {JAX FDM: A differentiable solver for inverse form-finding},
  author = {Pastrana, Rafael and Oktay, Deniz and Adams, Ryan P and Adriaenssens, Sigrid},
  journal = {arXiv preprint arXiv:2307.12407}
}