The machine learning methods, which have been adopted in the MGI, developed with big data and artificial intelligence. This chapter provides a brief overview of the machine learning methods adopted in the materials studies.doi:10.1007/978-3-030-68310-8_1Tian Wang
formation lithology classification: insights into machine learning methods The Proceeding of the SPE Annual Technical Conference and Exhibition (2019), 10.2118/196096-MS Calgary, Alberta, Canada, 30 September - 2 October. SPE-196096-MS Google Scholar Moussa et al., 2018a T. Moussa, S. Elkatatny...
an archetypal-complete problem, with the aid of machine learning (ML) techniques. Over the last decade, the machine learning society advances rapidly and surpasses human performance on several tasks. This trend also inspires a number of works that apply machine learning methods for SAT solving...
In recent years, two new techniques have emerged that have changed the way that atomistic simulations at electrochemical interfaces are approached. First, atomistic machine-learning methods have been developed1,2,3,4,5,6that use machine-learning surrogate models to quickly emulate and predict the re...
What is machine learning and why is it important? Learn how machine learning is already transforming our lives, and what are its limitations.
Examples of materials science that are suited for research using machine learning methods include the following properties [20], [21]: mechanical properties of composite materials, the glass transition temperature of oxide glasses, dielectric loss of polycrystalline materials over a wide frequency and ...
Within machine learning, deep learning14 has gained significant traction due to its ability to outperform traditional methods given larger data samples. Deep learning has played a significant role in advancements in fields such as medical computer vision15,16,17,18,19,20 and, more recently, in ...
TheshaprR package implements an enhanced version of the Kernel SHAP method, for approximating Shapley values, with a strong focus on conditional Shapley values. The core idea is to remain completely model-agnostic while offering a variety of methods for estimating contribution functions, enabling accur...
This study examines the effectiveness of state-of-the-art supervised machine learning methods in conjunction with different feature types for the task of automatic annotation of fragments of clinical text based on codebooks with a large number of categories. We used a collection of motivational inter...
By reviewing the fundamental knowledge of self-healing materials the typical machine learning methods in related studies, as well as performing several simulated experiments, three main conclusions can be summarized as follow: As for our future work, we would like to focus on further exploring the ...