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Physics-Guided Conditional Flow Matching with Energy Regularization for Robust PDE Inverse Problems
We consider partial differential equation (PDE) inverse problems from sparse, noisy, and corrupted observations, with the aim of recovering unknown coefficient fields and associated state variables in a mesh-free setting. Under such sparse and corrupted observations, standard physics-informed and ge
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