Submission + - "Shrinking bull's-eye" algorithm speeds up complex modeling from Days to Hours (youtube.com)
rtoz writes: To work with computational models is to work in a world of unknowns: Models that simulate complex physical processes — from Earth’s changing climate to the performance of hypersonic combustion engines — are complex, sometimes incorporating hundreds of parameters, each of which describes a piece of the larger process.
Parameters are often question marks within their models, their contributions to the whole largely unknown. To estimate the value of each unknown parameter requires plugging in hundreds of values, and running the model each time to narrow in on an accurate value. This computation can take days, and sometimes weeks.
Now MIT researchers have developed a new algorithm that vastly reduces the computation of virtually any computational model. The algorithm may be thought of as a shrinking bull’s-eye that, over several runs of a model, and in combination with some relevant data points, incrementally narrows in on its target: a probability distribution of values for each unknown parameter.
With this method, the researchers were able to arrive at the same answer as a classic computational approaches, but 200 times faster.
The researchers have applied the algorithm to a complex model for simulating movement of sea ice in Antarctica, involving 24 unknown parameters, and found that the algorithm is 60 times faster arriving at an estimate than current methods. They plan to test the algorithm next on models of combustion systems for supersonic jets.
Parameters are often question marks within their models, their contributions to the whole largely unknown. To estimate the value of each unknown parameter requires plugging in hundreds of values, and running the model each time to narrow in on an accurate value. This computation can take days, and sometimes weeks.
Now MIT researchers have developed a new algorithm that vastly reduces the computation of virtually any computational model. The algorithm may be thought of as a shrinking bull’s-eye that, over several runs of a model, and in combination with some relevant data points, incrementally narrows in on its target: a probability distribution of values for each unknown parameter.
With this method, the researchers were able to arrive at the same answer as a classic computational approaches, but 200 times faster.
The researchers have applied the algorithm to a complex model for simulating movement of sea ice in Antarctica, involving 24 unknown parameters, and found that the algorithm is 60 times faster arriving at an estimate than current methods. They plan to test the algorithm next on models of combustion systems for supersonic jets.
