Distributional inverse homogenization learns microstructural statistics from bulk mechanical measurements by inverting the homogenization process statistically.
Stochastic inverse problem: stability, regularization and wasserstein gradient flow.arXiv preprint arXiv:2410.00229
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A quadratic self-test loss derived from the weak-form evolution equation allows robust learning of particle interaction potentials directly from unlabeled data without trajectory recovery.
SCSI iteratively refines a self-consistent transport map to invert black-box corruptions and enable generative modeling of clean data.
Diffeomorphisms and vector fields are uniquely identifiable from finitely many pushforward densities or weighted divergences, with the number of required observations determined by embedding theorems.
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
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Distributional Inverse Homogenization
Distributional inverse homogenization learns microstructural statistics from bulk mechanical measurements by inverting the homogenization process statistically.
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Learning interacting particle systems from unlabeled data
A quadratic self-test loss derived from the weak-form evolution equation allows robust learning of particle interaction potentials directly from unlabeled data without trajectory recovery.
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Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants
SCSI iteratively refines a self-consistent transport map to invert black-box corruptions and enable generative modeling of clean data.
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On the Unique Recovery of Transport Maps and Vector Fields from Finite Measure-Valued Data
Diffeomorphisms and vector fields are uniquely identifiable from finitely many pushforward densities or weighted divergences, with the number of required observations determined by embedding theorems.
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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.