A rapid and effective method for alloy materials design via sample data transfer machine learning

$ 13.99

4.5
(181)
In stock
Description

Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods

Sensors, Free Full-Text

A rapid and effective method for alloy materials design via sample data transfer machine learning

Enhancing property prediction and process optimization in building materials through machine learning: A review - ScienceDirect

PDF) A Novel Method for Alloy Materials Design via Sample Data Transfer Machine Learning

Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics

The three-dimensional distribution of precipitates with the APT

PDF) Discovery of aluminum alloys with ultra-strength and high-toughness via a property-oriented design strategy

PDF) MLMD: a programming-free AI platform to predict and design materials

Materials, Free Full-Text

Characterizations of dislocations. (a) Dislocation density measured by

Research articles npj Computational Materials

Effect of moisture absorption of adhesive and CFRP on the failure of composite material adhesive joints

Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing

Material Forming - ESAFORM 2023 - Materials Research Forum