Monte Carlo methods based on the walk on spheres (WoS) algorithm offer a parallel, progressive, and output-sensitive approach for solving partial differential equations (PDEs) in complex geometric domains. Building on this foundation, the walk on stars (WoSt) method generalizes WoS to support mixed Dirichlet, Neumann, and Robin boundary conditions. However, accurately computing spatial derivatives of PDE solutions remains a major challenge: existing methods exhibit high variance and bias near the domain boundary, especially in Neumann-dominated problems. We address this limitation with a new extension of WoSt specifically designed for derivative estimation. Our method reformulates the boundary integral equation (BIE) for Poisson PDEs by directly leveraging the harmonicity of spatial derivatives. Combined with a tailored random-walk sampling scheme and an unbiased early termination strategy, we achieve significantly improved accuracy in derivative estimates near the Neumann boundary. We further demonstrate the effectiveness of our approach across various tasks, including recovering the non-unique solution to a pure Neumann problem with reduced bias and variance, constructing divergence-free vector fields, and optimizing parametrically defined boundaries under PDE constraints.

• Paper: pdf (30 MB)
• Source code: coming soon.

@article{Yu:2025:GradWost,
    author = {Yu, Z. and Sawhney, R. and Miller, B. and Wu, L. and Zhao, S.},
    title = {Robust Derivative Estimation with Walk on Stars},
    journal={ACM Trans. Graph.},
    volume={44},
    number={6},
    year={2025},
    pages = {253:1--253:16}
}

We thank the anonymous reviewers for their constructive feedback, and Ken Museth and Aaron Lefohn for their support. This work began while Zihan Yu and Bailey Miller were interns at NVIDIA, and is partially supported by NSF grants 2239627 and 2504890, and a National Institute of Food and Agriculture award 2023-67021-39073.