AI RESEARCH
Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
arXiv CS.LG
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ArXi:2606.01110v1 Announce Type: cross Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informed neural networks (PINNs) and their domain-decomposed variants (FBPINNs) offer a mesh-free alternative but face convergence challenges when representing complex velocity fields.