A new arXiv preprint presents a mesh-based super-resolution method that uses graph transformers to reconstruct multiscale detonation flow data. The authors argue such data-driven reconstruction is useful for subgrid closure modeling, faster spatiotemporal forecasting, compression, and as an upsampling step in simulations. The work appears as a cross-listed revision in the cs.AI and cs.LG categories.
Researchers introduce the Variational Incompressible Optimal Transport (VIOT) operator, a generative neural operator designed to transport densities in an amortized way. Given a new pair of source and target densities, VIOT outputs a divergence-free velocity field to carry out the transformation. The approach targets incompressible flow settings, where the velocity field must remain divergence-free.
A revised arXiv preprint examines why neural network subgrid stress models perform well in a priori tests but deteriorate in a posteriori large eddy simulations. The authors propose approaches to reduce this discrepancy so that model evaluation better reflects real simulation behavior. The work sits in computational fluid dynamics research rather than commercial AI deployment.