The widely accepted intuition that the important properties of solids are determined by a few key variables underpins many methods in physics. Though this reductionist paradigm is applicable in many physical problems, its utility can be limited because the intuition for identifying the key variables often does not exist or is difficult to develop. Machine learning algorithms (genetic programming, neural networks, Bayesian methods, etc.) attempt to eliminate the a priori need for such intuition but often do so with increased computational burden and human time. A recently developed technique in the field of signal processing, compressive sensing (CS), provides a simple, general, and efficient way of finding the key descriptive variables. CS is a powerful paradigm for model building; we show that its models are more physical and predict more accurately than current state-of-the-art approaches and can be constructed at a fraction of the computational cost and user effort.
Physical Review B
- Pub Date:
- January 2013
- Theories and models of many-electron systems;
- Other topics in materials science;
- Condensed Matter - Materials Science
- First arXiv submission: 7 pages, 4 figures, submitted to PRB. Second arXiv submission:14 pages, 7 figures. Significant changes made to the text in this revision but content and science is the same, presentation is merely improved. More mathematical detail, clearer discussion of the outcomes, etc