Parameter identification for phase-field modeling of fracture: a Bayesian approach with sampling-free update

Abstract

Phase-field modeling of fracture has gained popularity within the last decade due to the flexibility of the related computational framework in simulating three-dimensional arbitrarily complicated fracture processes. However, the numerical predictions are greatly affected by the presence of uncertainties in the mechanical properties of the material originating from unresolved heterogeneities and the use of noisy experimental data. The objective of this work is to apply the Bayesian approach to estimate bulk and shear moduli, tensile strength and fracture toughness of the phase-field model, thus improving accuracy of the simulations with the help of experimental data. Conventional approaches for estimating the Bayesian posterior probability density function adopt sampling schemes, which often require a large amount of model estimations to achieve the desired convergence, thus resulting in a high computational cost. In order to alleviate this problem, we employ a more efficient approach called sampling-free linear Bayesian update, which relies on the evaluation of the conditional expectation of parameters given experimental data. We identify the mechanical properties of cement mortar by conditioning on the experimental data of the three-point bending test (observations) in an online and offline manner. In the online approach the parameter values are sequentially updated on the fly as the new experimental information comes in. In contrast, the offline approach is used only when the whole history of experimental data is provided once the experiment is performed. Both versions of estimation are discussed and compared by validating the phase-field fracture model on an unused set of experimental data.

Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator–regression neural network

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Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network

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Space-time formulation, discretization, and computational performance studies for phase-field fracture optimal control problems

“…First results on mathematical and numerical analysis were recently established in [39,40,38,23]. Computational studies were undertaken in parameter estimation [28,48,41,42], stochastic modeling [21], topology optimization [16], and on optimal control in our prior work [27]. Solving phase-field fracture problems using methods from shape optimization was proposed in [1].…”