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arXiv Machine Learning By Tatthapong Srikitrungruang, Jaesung Lee

Probabilistic Physics-Informed Neural Networks for Estimating Heterogeneous Elastic Properties from Low-Resolution and Noisy Displacement Data

Read the original on arXiv Machine Learning →

arXiv:2607. 14563v1 Announce Type: new Abstract: Estimating spatially heterogeneous elastic properties from low-resolution displacement measurements is a severely ill-posed inverse elasticity problem because low resolution obscures spatial details needed to distinguish heterogeneous property variations, and small measurement perturbations or fitting errors are amplified through inverse estimation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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