Scale-autoregressive modeling (SAR) samples fluid flow distributions hierarchically from coarse to fine resolutions on meshes, achieving lower distributional error and 2-7x faster runtime than diffusion or flow-matching baselines.
Benchmarking autoregressive conditional diffusion models for turbulent flow simulation
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FD-Bench supplies the first modular, reproducible benchmark and leaderboard for comparing neural PDE solvers on fluid dynamics tasks with direct numerical solver baselines.
PODR precomputes a proper orthogonal decomposition basis from classical solutions to project quantum states onto a minimal set of coefficients for reconstruction, reducing measurements in online quantum simulations.
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One Scale at a Time: Scale-Autoregressive Modeling for Fluid Flow Distributions
Scale-autoregressive modeling (SAR) samples fluid flow distributions hierarchically from coarse to fine resolutions on meshes, achieving lower distributional error and 2-7x faster runtime than diffusion or flow-matching baselines.
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FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
FD-Bench supplies the first modular, reproducible benchmark and leaderboard for comparing neural PDE solvers on fluid dynamics tasks with direct numerical solver baselines.
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Problem-Specific Basis Quantum State Readout via Proper Orthogonal Decomposition
PODR precomputes a proper orthogonal decomposition basis from classical solutions to project quantum states onto a minimal set of coefficients for reconstruction, reducing measurements in online quantum simulations.