Using Interval Constraint Solving Techniques to better understand and predict future behaviors of dynamic problems

The ability to make observations of natural phenomena has played a fundamental role in our world. From what we observe, models are derived and we can get an understanding about how things work by simulating our models. This has been particularly important in areas such as medicine, physics, chemistry. However, when we do not initiate simulations but that we are simply observing a phenomenon, it is valuable to be able to understand it “on the fly” and be able to predict its future behavior. Added challenges come from the fact that observations are never 100% accurate and therefore we must deal with uncertainty. In this work, we use Interval Constraint Solving Techniques (ICST) to handle uncertainty in the observations of a given phenomenon, and to be able to determine its initial conditions and unfold the dynamic behavior further in time.