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Simulation and computation play a critical role in important societal problems. Examples include the role of anthropogenic emissions on climate and ocean circulation; the prediction of earthquakes and tsunamis; the prediction of paths and storm surges of hurricanes; designing infrastructure that is capable of withstanding disasters, such as floods and terrorist attacks; the design and long term durability of major infrastructure, such as bridges, tunnels, etc; the spread and containment of disease and epidemics, etc. These systems exhibit extreme complexity: there are a myriad of different issues that are critical and must be accurately addressed. Models contain and interface large numbers of physical effects. All of these problems represent grand challenge computer problems that require pushing the limits of technology, both with regards to algorithms and machines as well as the development of the physical models themselves.
Critical issues include the development and coupling of algorithms for multiphysics, multiscale applications, verification and validation of these computer models, and quantifying their predictive reliability, given potentially large uncertainty in numerical accuracy, code reliability and ultimately the models themselves. How good is good enough? For any complex phenomenon, as data and computing power increases, more and more physical effects can always be included, and larger computations can always be designed. However there will always be substantial uncertainty and numerical error to overcome.
This workshop will have two main parts: first we will overview the major computational efforts in a number of different problems of societal importance, including climate, atmospheric pollution, floods and earthquakes, alternative energy sources and carbon sequestration. Speakers will assess the state of the art, highlighting the major areas of current research in algorithms, data collection and integration, and error and uncertainty estimation. The second part will include researchers working on mathematical foundations of computer architectures, algorithms, error estimation and uncertainty quantification methods. This will repeat (in small doses) some of the discussion previously taking place in this annual year but the hope is that we will excite a synergy between the mathematicians and the practitioners to find areas where real progress can be made.
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