The algorithm was developed by applying scientific principles used to create models for understanding cell biology and physics to the challenges of cosmology and big data, according to the study published in the journal Proceedings of the National Academy of Sciences.
"Science works because things behave much more simply than they have any right to, very complicated things end up doing rather simple collective behaviour," said James Sethna, Professor at the Cornell University, US.
The algorithm allows researchers to image a large set of probabilities to look for patterns or other information that might be useful, and provides them with better intuition for understanding complex models and data.
In addition to cosmology, their model has applications to Machine Learning and statistical physics, which also work in terms of predictions.
To test the algorithm, the researchers used data from the European Space Agency's Planck satellite, and studied it.
They applied the model data on the cosmic microwave background - radiation left over from the universe's early days.
The model produced a map depicting possible characteristics of different universes, of which our own universe is one point.
"This new method of visualising the qualities of our universe highlights the hierarchical structure of the dark energy and dark matter dominated model that fits the cosmic microwave background data so well," said study co-author Michael Niemack.
"These visualisations present a promising approach for optimising cosmological measurements in the future," he added.
( With inputs from IANS )