Adjoint PINN surrogates are constructed to evolve runaway electron fluid moments and distributions for arbitrary initial conditions, achieving orders-of-magnitude speedup over conventional RE solvers with reported validation agreement.
Towards Physics-informed Deep Learning for Turbulent Flow Prediction , booktitle =
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GNN-based MD simulators achieve stable structure-only initialization and reliable OOD generalization through inference-time physics optimization and a GNN barostat on elastic network compression tasks.
PnP-Corrector decouples pre-trained physics engines from a correction agent to mitigate reciprocal error amplification in coupled spatiotemporal forecasting, cutting error by 28% on a 300-day ocean-atmosphere task.
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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Hierarchical Framework of Runaway Electrons using Deep Learning
Adjoint PINN surrogates are constructed to evolve runaway electron fluid moments and distributions for arbitrary initial conditions, achieving orders-of-magnitude speedup over conventional RE solvers with reported validation agreement.