PI-DIONs train DeepONet-style networks to invert PDEs using only physics residuals and partial measurements, with no labeled (measurement, parameter) pairs.
The final embedding is obtained by computing the inner product of their outputs
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Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
PI-DIONs train DeepONet-style networks to invert PDEs using only physics residuals and partial measurements, with no labeled (measurement, parameter) pairs.