LLMs prompted with domain knowledge can generate runnable, numerically valid code for stiff and non-stiff ODEs on new diagnostic and 1000-task benchmarks.
Neural operator: Graph kernel network for partial differential equations
2 Pith papers cite this work. Polarity classification is still indexing.
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An ALE-consistent GNO-ViT and LSTM framework with boundary correction and two-stage training achieves accurate phase-consistent long-term FSI predictions on a flexible beam benchmark with good generalization to inlet variations.
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SciML Agents: Write the Solver, Not the Solution
LLMs prompted with domain knowledge can generate runnable, numerically valid code for stiff and non-stiff ODEs on new diagnostic and 1000-task benchmarks.
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An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction
An ALE-consistent GNO-ViT and LSTM framework with boundary correction and two-stage training achieves accurate phase-consistent long-term FSI predictions on a flexible beam benchmark with good generalization to inlet variations.