SpUDD defines superpower contours from power diagrams of unsigned distance samples, proves convergence to the true surface, and uses them to generate approximating polygonal meshes that outperform prior strategies.
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MC² corrects low-budget Monte Carlo solutions for elliptic PDEs with a single-pass neural network to match the accuracy of 1000× more Monte Carlo samples while outperforming classical and learned baselines.
Branching path statistics are cast into Navier-Stokes nonlinear transport to produce new propagator representations and backward Monte Carlo algorithms for confined fluid flows.
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SpUDD: Superpower Contouring of Unsigned Distance Data
SpUDD defines superpower contours from power diagrams of unsigned distance samples, proves convergence to the true surface, and uses them to generate approximating polygonal meshes that outperform prior strategies.
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MC$^2$: Monte Carlo Correction for Fast Elliptic PDE Solving
MC² corrects low-budget Monte Carlo solutions for elliptic PDEs with a single-pass neural network to match the accuracy of 1000× more Monte Carlo samples while outperforming classical and learned baselines.
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Branching Paths Statistics for confined Flows : Adressing Navier-Stokes Nonlinear Transport
Branching path statistics are cast into Navier-Stokes nonlinear transport to produce new propagator representations and backward Monte Carlo algorithms for confined fluid flows.