Distributional inverse homogenization learns microstructural statistics from bulk mechanical measurements by inverting the homogenization process statistically.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
A neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness.
A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.
citing papers explorer
-
Distributional Inverse Homogenization
Distributional inverse homogenization learns microstructural statistics from bulk mechanical measurements by inverting the homogenization process statistically.
-
Physics-informed neural particle flow for the Bayesian update step
A neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness.
-
Efficient Deconvolution in Populational Inverse Problems
A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.