Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression
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Publication:2175100
DOI10.1016/j.cma.2019.112724zbMath1441.74128OpenAlexW2994789389WikidataQ99629395 ScholiaQ99629395MaRDI QIDQ2175100
Adrian Buganza Tepole, Taeksang Lee, Ilias Bilionis
Publication date: 28 April 2020
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453758
Related Items (8)
Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues ⋮ Data-driven computational method for growth-induced deformation problems of soft materials ⋮ Model-data-driven constitutive responses: application to a multiscale computational framework ⋮ Predicting the mechanical properties of biopolymer gels using neural networks trained on discrete fiber network data ⋮ Editorial: Special issue on uncertainty quantification, machine learning, and data-driven modeling of biological systems ⋮ Statistical test for anomalous diffusion based on empirical anomaly measure for Gaussian processes ⋮ Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations ⋮ Discriminating Gaussian processes via quadratic form statistics
Uses Software
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