The authors develop an input-schema identifiability certificate for physics-informed surrogates that decomposes lumen velocity in tubular flow into mesh-measurable tangent direction, boundary-condition-dependent magnitude, and signed-orientation ambiguity using a Cosserat-rod reduction.
W., Fang, Z., Bansil, A.et al.Accurate prediction of tensorial spectra using equivariant graph neural network.Nat
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Input-schema identifiability limits in physics-informed surrogates for mechanics-governed flow
The authors develop an input-schema identifiability certificate for physics-informed surrogates that decomposes lumen velocity in tubular flow into mesh-measurable tangent direction, boundary-condition-dependent magnitude, and signed-orientation ambiguity using a Cosserat-rod reduction.