The Computer Engineering 2017 conference will bring together experts in mathematical modeling, numerical analysis, numerical software engineering, and statistics, along with scientists from a variety of key applications to assess our current ability to quantify uncertainty in modeling and simulation, to raise awareness of this issue within the numerical software community, and to help envision a research agenda to address this critical need. Conference topics will include: (a) Numerical software verification, (b) Validation metrics and comparison with physical experiment, and (c) Uncertainty estimation for predictive modeling and simulation. In addition, case studies from representative application areas, such as electromagnetics, mechanical engineering, and nuclear power plant control, will be presented.
Computing has become an indispensable component of modern science and engineering research. As has been repeatedly observed and documented, processing speed measured in floating point operations per second has experienced exponential growth for several decades. These hardware efficiencies have been accompanied by innovations in mathematical algorithms, numerical software, and programming tools. The result is that, by any measure, the modern computer is many orders of magnitude more powerful than its early predecessors, capable of simulating physical problems of unprecedented complexity.
Given the success of scientific computation as a research tool, it is natural that scientists, engineers, and policy makers strive to harness this immense potential by using computational models for critical decision-making. Increasingly, computers are being used to supplement experiments, to prototype engineering systems, or to predict the safety and reliability of high-consequence systems. Such use inevitably leads one to question "How good are these simulations? Would you bet your life on them?" Unfortunately, most computational scientists today are ill equipped to address such important questions with the same scientific rigor that is routine in experimental science.