Self-attention mechanisms are used to build mesh-preserving neural surrogates that approximate PFEM dynamics for free-surface flows, delivering accurate transient predictions and improved scalability on 2D and 3D benchmarks.
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2 Pith papers cite this work. Polarity classification is still indexing.
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SAT-RTS introduces a pipeline that abstracts high-dimensional RTS sequences into discrete tactical labels and hierarchical visualizations to improve interpretability of AI micromanagement.
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Attention mechanism for scalable mesh-based neural surrogates of free-surface fluids
Self-attention mechanisms are used to build mesh-preserving neural surrogates that approximate PFEM dynamics for free-surface flows, delivering accurate transient predictions and improved scalability on 2D and 3D benchmarks.
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SAT-RTS: A systematic framework for tactical knowledge extraction and visualization-based analysis in real-time strategy games
SAT-RTS introduces a pipeline that abstracts high-dimensional RTS sequences into discrete tactical labels and hierarchical visualizations to improve interpretability of AI micromanagement.