We have 3 of 19 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Predicting transient urban winds is fundamental to understanding urban microclimates and designing climate-resilient cities. Building-resolving large-eddy simulation produces detailed incompressible urban wind fields at substantial computational cost for each layout. Neural surrogates offer a faster alternative by lear...
This work proposes Aggregation-Aware Representation Learning (AARL) to learn numerically stable and cancellation-resistant representations without sacrificing expressiveness, and formalizes this through the cancellation ratio, proving that it is fundamentally governed by graph topology.
This work proposes Aggregation-Aware Representation Learning (AARL) to learn numerically stable and cancellation-resistant representations without sacrificing expressiveness, and formalizes this through the cancellation ratio, proving that it is fundamentally governed by graph topology.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
