Quantitative predictions of natural and induced phenomena in fractured rock is one of the
great challenges in the Earth and Energy Sciences with far‐reaching economic and …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

The present work aims at the identification of the effective constitutive behavior of Σ 5 Σ 5
aluminum grain boundaries (GB) for proportional loading by using machine learning (ML) …

Interfacial mechanical properties are important in composite materials and their applications,
including vehicle structures, soft robotics, and aerospace. Determination of traction …

In this paper, a data-driven enhanced combined finite-discrete element method (DDFDEM)
is proposed to simulate the rock mechanical behavior by directly assigning the rock …

The battery cell performance is determined by electro-chemo-mechanical mechanisms on
different length scales. Though there exist multi-field multiscale simulation frameworks, the …

A critical part of failure analysis is to understand the fracture process from initiation through
crack propagation. Crack propagation in brittle materials can produce crack branching …

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate
modeling approach that can provide rapid predictions of microstructural evolution and the …

In this work we use the peridynamics theory of solid mechanics to simulate fracture in an
annular rock domain subject to an in-situ stress and create surrogate models that predict the …

Experimental research of bone strength remains costly and limited for ethical and technical
reasons. Therefore, to predict the mechanical state of bone tissue, as well as similar …