beyucel / fmks

Functional Materials Knowledge System

^{fMKS – Functional Materials Knowledge System}

MKS

The Materials Knowledge Systems (MKS) is a novel data science approach for solving multiscale materials science problems. It uses techniques from physics, machine learning, regression analysis, signal processing, and spatial statistics to create processing-structure-property relationships. The MKS carries the potential to bridge multiple length scales using localization and homogenization linkages, and provides a data driven framework for solving inverse material design problems.

See these references for further reading:

• Computationally-Efficient Fully-Coupled Multi-Scale Modeling of Materials Phenomena Using Calibrated Localization Linkages, S. R. Kalidindi; ISRN Materials Science, vol. 2012, Article ID 305692, 2012, doi:10.5402/2012/305692.

• Formulation and Calibration of Higher-Order Elastic Localization Relationships Using the MKS Approach, Tony Fast and S. R. Kalidindi; Acta Materialia, vol. 59 (11), pp. 4595-4605, 2011, doi:10.1016/j.actamat.2011.04.005

• Developing higher-order materials knowledge systems, T. N. Fast; Thesis (PhD, Materials engineering)--Drexel University, 2011, doi:1860/4057.

fMKS

fMKS is a functional version of PyMKS currently under development. The purpose of the project is to prototype a parallel implementation of MKS using functional programming in Python primarily using the Toolz library.