3 papers
math.NA2026
Property-Preserving Data Compression for Incompressible Flows
Andrey Pluzhnik, Ramansh Sharma, Martin Burtscher +1
We develop a method for lossy compression of incompressible (i.e., divergence-free) velocity fields that preserves zero divergence and, on periodic domains, periodicity. We fit the…
cs.DC2025
VLCs: Managing Parallelism with Virtualized Libraries
Yineng Yan, William Ruys, Hochan Lee +11
As the complexity and scale of modern parallel machines continue to grow, programmers increasingly rely on composition of software libraries to encapsulate and exploit parallelism.…
cs.LG2025
Local-Order Auxiliary Losses Can Improve Autoencoder Reconstruction
Harvey Dam, Martin Burtscher, Tripti Agarwal +1
Mean-squared error is the default objective for training autoencoders, yet compressed reconstructions often depend not only on pointwise accuracy but also on preserving local spati…