The computer scientist Chris Wojtan has been awarded with an ERC Starting Grant for his project “Big Splash: Efficient Simulation of Natural Phenomena at Extremely Large Scales”, resulting in a total number of 15 ERC grantees at IST Austria, of 31 professors currently on campus. President Thomas A. Henzinger: “The ERC grant for Chris illustrates the continuing attraction of IST Austria for extraordinarily talented scientists in general and the strength of the computer scientists in particular. Now, all six computer scientists at IST Austria have been awarded with this prestigious grant.” Wojtan joined IST Austria in February 2011. The ERC Starting Grant is funded with € 1,5 mio for five years and will start in 2015.

Computational simulations of natural phenomena are essential in science, engineering, product design, architecture, and computer graphics applications. However, despite progress in numerical algorithms and computational power, it is still unfeasible to compute detailed simulations at large scales. To make matters worse, important phenomena like turbulent splashing liquids and fracturing solids rely on delicate coupling between small-scale details and large-scale behavior. Brute-force computation of such phenomena is intractable, and current adaptive techniques are too fragile, too costly, or too crude to capture subtle instabilities at small scales. Increases in computational power and parallel algorithms will improve the situation, but progress will only be incremental until we address the problem at its source.

Wojtan applies two main approaches to this problem of efficiently simulating large-scale liquid and solid dynamics. His first avenue of research combines numerics and shape by investigating a careful de-coupling of dynamics from geometry, allowing essential shape details to be preserved and retrieved without wasting computation. The second main research direction is the manipulation of large-scale simulation data: Given the redundant and parallel nature of physics computation, Wojtan intends to drastically speed up computation with novel dimension reduction and data compression approaches, thus minimizing unnecessary computation by re-using existing simulation data. The novel approaches resulting from this work are intended to improve simulations and contribute to understand complicated natural and biological processes that are presently unfeasible to compute.

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