Research

Sparse Autoencoders as a Steering Basis for Phase Synchronization in Graph-Based CFD Surrogates

Sparse autoencoders enable interpretable, fine-grained steering of graph-based CFD surrogates—offering a mechanistic interpretability approach to control neural physics simulations.

Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline

Research paper proposing sparse autoencoders as a steering mechanism for computational fluid dynamics surrogates with phase synchronization in graph-based models.

Read original at arXiv CS.LG (Machine Learning)

New Gas-Powered Data Centers Could Emit More Greenhouse Gases Than Whole Nations

Stargate and Project Jupiter's gas-powered AI data centers will emit 24+ million tons of greenhouse gases annually, rivaling entire nations' emissions and forcing regulatory reckoning with AI expansion's climate cost.