Determining useful data
Finding useful data within enormous sets of data is a difficult task. A team at Princeton University has been formed with the task of visualizing physics data to make it easier to find useful data [50]. Within these visualizations is an enormous amount of data that is still difficult to sift through. For example, a climate model could show the prediction of a tornado somewhere in the mid-western United States. Many people must go over this data to find the tornado in order for the data to be useful. This manual process could result in weather anomalies that are not predicted because humans cannot reliably monitor all of the data all of the time as history has proven. So, a remaining challenge is to find a process that accurately and consistently finds these anomalies in data without human intervention.
Soft-body physics
It is difficult to make soft-body physics computer simulations as accurate as a real-world simulation of the same phenomenon. No exact numbers can be found on the accuracy difference between computer simulation and real-world simulation in this field because there are so many different methods of simulations that result in varying degrees of accuracy [49]. What is generally agreed upon is that computer simulations are not as accurate as real world simulations. With current technology, because of the inaccuracy of computer simulation, there will always be a need for real-world simulation. Thus, it will always be a challenge to make soft-body physics models more accurate to eventually phase out real world simulation.
Aerodynamics
Similar to the problems within soft-body physics problems arise within the field of aerodynamics. The physics-based computer simulations are scalable simulations. They can be made as large or as small as the scientist wants. Often, the scientist wants significantly larger and more accurate simulations [50]. This is especially true within the field of aerodynamics where the computer models could replace costly experiments if they are deemed accurate enough. Because these simulations need to be scalable, there will always be the challenge to make computing power meet the accuracy and size demands of the scientists.
Massive data sets
Most of the future issues that have been shown have to do with one thing, massive data sets. One of the biggest challenge with computational physics dealing with big data is massive data sets. The data sets will continue to grow as they have in the past [51]. The need for more computing power and different methods for dealing with that computing power will always be present and growing.