A multi-branch CNN fed with the trEFM frequency trace and cantilever parameters extracts bi-exponential kinetic parameters (τ1, τ2, A) more accurately and noise-robustly than the prior single-exponential neural network.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.mtrl-sci 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Multi-Output Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data
A multi-branch CNN fed with the trEFM frequency trace and cantilever parameters extracts bi-exponential kinetic parameters (τ1, τ2, A) more accurately and noise-robustly than the prior single-exponential neural network.