μ-FlowNet applies an attention U-Net to map flow fields in irregular microchannels, reporting dice score 0.9317 and IoU 0.8731 on test data while outperforming standard U-Net and T-Net.
High -order splitting methods for the incompressible Navier-Stokes equations
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Implicit velocity correction schemes extend the stable time step by up to two orders of magnitude and reduce overall time-to-solution by up to a factor of eleven for high-Re incompressible flow simulations, with only minor accuracy loss up to 20 times the explicit limit.
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$\mu$-FlowNet: A Deep Learning Approach for Mapping Flow Fields in Irregular Microchannels Using an Attention-based U-Net Encoder-Decoder Architecture
μ-FlowNet applies an attention U-Net to map flow fields in irregular microchannels, reporting dice score 0.9317 and IoU 0.8731 on test data while outperforming standard U-Net and T-Net.
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Implicit Velocity Correction Schemes for Scale-Resolving Simulations of Incompressible Flow: Stability, Accuracy, and Performance
Implicit velocity correction schemes extend the stable time step by up to two orders of magnitude and reduce overall time-to-solution by up to a factor of eleven for high-Re incompressible flow simulations, with only minor accuracy loss up to 20 times the explicit limit.