VesNet hybrid NN-conventional solver for 2D Stokesian vesicle suspensions achieves over 100x speedup while capturing key dynamics in single, pair, and large-scale flows.
arXiv preprint arXiv:2411.09678 , year=
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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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VesNet: Neural network accelerated solver for simulating Stokesian vesicle suspensions
VesNet hybrid NN-conventional solver for 2D Stokesian vesicle suspensions achieves over 100x speedup while capturing key dynamics in single, pair, and large-scale flows.
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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.