A directional GNN combined with constrained PPO jointly improves flow-field reconstruction accuracy and sensor layout selection in realistic fluid dynamics settings.
Operator learning for reconstructing flow fields from sparse measurements: An energy transformer approach
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A language model-based operator learning method reconstructs flow fields from under 10% sparse measurements on vortex street, US temperature, blood flow, and turbulent jet benchmarks with competitive accuracy.
The paper reviews and extends energy-based dynamical models that use gradient flows and energy landscapes for neurocomputation, learning, and optimization tasks.
citing papers explorer
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Flow Field Reconstruction with Sensor Placement Policy Learning
A directional GNN combined with constrained PPO jointly improves flow-field reconstruction accuracy and sensor layout selection in realistic fluid dynamics settings.
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Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach
A language model-based operator learning method reconstructs flow fields from under 10% sparse measurements on vortex street, US temperature, blood flow, and turbulent jet benchmarks with competitive accuracy.
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Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
The paper reviews and extends energy-based dynamical models that use gradient flows and energy landscapes for neurocomputation, learning, and optimization tasks.