A three-network PINN with decoupled strain prediction and boundary-force calibration recovers absolute-scale heterogeneous Young's modulus and Poisson's ratio from noisy synthetic displacement data.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Robust Physics-Informed Neural Network Approach for Estimating Heterogeneous Elastic Properties from Noisy Displacement Data
A three-network PINN with decoupled strain prediction and boundary-force calibration recovers absolute-scale heterogeneous Young's modulus and Poisson's ratio from noisy synthetic displacement data.