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.
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2026 2verdicts
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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
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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.
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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.