arXiv subjects

Zhuohan Li

Publications and source records attributed to Zhuohan Li.

38 records · Page 3Linked to original sources

arXiv2018

Multiphysics model of chemical aging in frictional contacts

An increase of static friction during stationary contacts of two solids due to interfacial chemical bonding has been reported in multiple experiments. However, the physics underlying such frictional aging is still not fully understood because it involves multiple physical and chemical effects coupled with each other, making direct interpretation of experimental results difficult. Here, we develop a multiphysics chemical aging model that combines contact mechanics, mechanochemistry, and interfacial chemical reaction kinetics. Our model predicts that aging is proportional to normal loads in a low-load regime and becomes nonlinear at higher loads. We also discovered a nonmonotonic temperature dependence of aging with a peak near room temperature. In addition, our simulations provide insights into contributions from specific physical/chemical effects on the overall aging. Our model shows quantitative agreement with available single-asperity experiments on silica-silica interfaces, and it provides a framework for building a chemical aging model for other material systems with arbitrary types of physical and chemical effects involved.

cond-mat.soft↗

arXiv2018

Towards Binary-Valued Gates for Robust LSTM Training

Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling. It aims to use gates to control information flow (e.g., whether to skip some information or not) in the recurrent computations, although its practical implementation based on soft gates only partially achieves this goal. In this paper, we propose a new way for LSTM training, which pushes the output values of the gates towards 0 or 1. By doing so, we can better control the information flow: the gates are mostly open or closed, instead of in a middle state, which makes the results more interpretable. Empirical studies show that (1) Although it seems that we restrict the model capacity, there is no performance drop: we achieve better or comparable performances due to its better generalization ability; (2) The outputs of gates are not sensitive to their inputs: we can easily compress the LSTM unit in multiple ways, e.g., low-rank approximation and low-precision approximation. The compressed models are even better than the baseline models without compression.

cs.LG↗