Neural equal-area scattering surrogate for He-Ar preserves QD, Qμ, RCS and related quantities within 1.5% and reproduces DSMC diffusion and shear tests within 1-2% normalized error.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
GQPINNs add symmetry awareness to quantum PINNs via equivariant circuits, yielding lower mean absolute error and fewer parameters than standard QPINNs on linear and nonlinear PDE benchmarks.
PIC-Flow applies conditional flow matching with a real-valued U-Net and interface-masked Helmholtz residual loss to predict electromagnetic fields in photonic devices, generalizing to held-out device classes beyond its training set.
citing papers explorer
-
Transport-preserving neural ab initio scattering kernels for rarefied binary gas mixtures
Neural equal-area scattering surrogate for He-Ar preserves QD, Qμ, RCS and related quantities within 1.5% and reproduces DSMC diffusion and shear tests within 1-2% normalized error.
-
Geometric Quantum Physics Informed Neural Network
GQPINNs add symmetry awareness to quantum PINNs via equivariant circuits, yielding lower mean absolute error and fewer parameters than standard QPINNs on linear and nonlinear PDE benchmarks.
-
Physics-Based Flow Matching for Full-Field Prediction of Silicon Photonic Devices
PIC-Flow applies conditional flow matching with a real-valued U-Net and interface-masked Helmholtz residual loss to predict electromagnetic fields in photonic devices, generalizing to held-out device classes beyond its training set.