A Fourier neural operator trained on Boussinesq-compressible simulation pairs corrects Boussinesq predictions for natural convection, achieving SSIM near unity and MSE reductions of one to three orders of magnitude.
Lanier, Learning to precondition: Reinforcement learning enhanced three-level circulant preconditioning for the discrete dipole approxima- tion, J
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
physics.comp-ph 2years
2026 2verdicts
CONDITIONAL 2representative citing papers
A unified methodology achieves floating-point consistent results across DDSCAT, ADDA, and IFDDA solvers and enables fair CPU/GPU benchmarking with provided equivalence tables and software.
citing papers explorer
-
A Neural Surrogate Approach for Simulating Natural Convection Problems
A Fourier neural operator trained on Boussinesq-compressible simulation pairs corrects Boussinesq predictions for natural convection, achieving SSIM near unity and MSE reductions of one to three orders of magnitude.
-
Floating-point consistent cross-verification methodology for reproducible and interoperable DDA solvers with fair benchmarking
A unified methodology achieves floating-point consistent results across DDSCAT, ADDA, and IFDDA solvers and enables fair CPU/GPU benchmarking with provided equivalence tables and software.