A team at Stanford has shown that using fewer, higher-quality data points can speed up complex simulations.
A team at Stanford has shown that using fewer, higher-quality data points can speed up complex simulations. The method could impact fields from aircraft certification to climate modeling.
Anyone who has seen a fluid mechanics simulation in action, relied on a weather model to anticipate an oncoming hurricane, or seen a flight simulator put a virtual, billion-dollar jet design through its paces, knows that the mathematics of simulation are almost impossibly complex. Even on the fastest supercomputers, these calculations can sometimes take days to complete.
Now, researchers at Stanford University have discovered a way to not only expedite modeling calculations but actually produce better results by gathering fewer but higher-quality data and removing redundancies that bog down traditional approaches. They achieved this by using a new approach that simplifies the data inputs to speed calculations.
“The key takeaway from this work is that, by being smart about what data we collect, we can significantly reduce the amount of data required to construct accurate models of complex systems,” said Joshua Ott, a doctoral student in aeronautics and astronautics and first author of the new paper appearing at the Learning for Dynamics & Control Conference (L4DC). “If you can simplify the data inputs, you make the math easier.”
Read more at Stanford University
