Reformulates CFD inference as self-supervised inpainting on tokenized velocity fields to produce reusable flow priors that handle boundary and geometry shifts better than supervised surrogates.
Yuval Eldar, Michael Lindenbaum, Moshe Porat, and Yehoshua Y Zeevi
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Conditional neural fields combined with LSTM networks predict aircraft ditching loads accurately across heterogeneous spatial discretizations using fewer parameters than convolutional autoencoders.
citing papers explorer
-
Inpainting physics: self-supervised learning for context-driven fluid simulation
Reformulates CFD inference as self-supervised inpainting on tokenized velocity fields to produce reusable flow priors that handle boundary and geometry shifts better than supervised surrogates.
-
Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction
Conditional neural fields combined with LSTM networks predict aircraft ditching loads accurately across heterogeneous spatial discretizations using fewer parameters than convolutional autoencoders.