McMg is a learned phase-space multi-channel multigrid preconditioner that maps residuals to corrections for heterogeneous Helmholtz equations and shows fewer iterations than classical and neural baselines in tests.
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Precomputed hierarchical geometry tokens plus a decoupled decoder enable competitive neural PDE accuracy on industrial 3D meshes exceeding 10M nodes with linear memory scaling.
AeroJEPA applies joint-embedding predictive learning to produce scalable, semantically organized latent representations for 3D aerodynamic fields that support both field reconstruction and downstream design tasks.
NOTES couples a DeepONet topology decoder with CMA-ES in a PCA-derived latent space, achieving >95% deflection efficiency on nanophotonic metagratings and compliance of 246 on MBB beams, outperforming direct CMA-ES and gradient-based baselines.
citing papers explorer
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McMg: A Learned Phase-Space Multi-channel Multigrid Preconditioner for Helmholtz Equation
McMg is a learned phase-space multi-channel multigrid preconditioner that maps residuals to corrections for heterogeneous Helmholtz equations and shows fewer iterations than classical and neural baselines in tests.
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PGD-NO: A Neural Operator with Precomputed Geometry Decomposition for 3D Million-scale Physics Simulations
Precomputed hierarchical geometry tokens plus a decoupled decoder enable competitive neural PDE accuracy on industrial 3D meshes exceeding 10M nodes with linear memory scaling.
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AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling
AeroJEPA applies joint-embedding predictive learning to produce scalable, semantically organized latent representations for 3D aerodynamic fields that support both field reconstruction and downstream design tasks.
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Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
NOTES couples a DeepONet topology decoder with CMA-ES in a PCA-derived latent space, achieving >95% deflection efficiency on nanophotonic metagratings and compliance of 246 on MBB beams, outperforming direct CMA-ES and gradient-based baselines.