Science

Reactive Systems are formal systems that cause events in the physical world, in reaction to a set of monitored inputs. Examples include control systems for aircraft, medical devices, industrial processes, and consumer appliances. In many of these examples, safety (and often security) of the system is of paramount importance. To say anything at all about whether such a system is safe or secure, one has to be able to predict its behavior under the conditions that the system may encounter in use.

Algorithms for solving difficult computational problems play a key role in many applications, including scheduling, resource allocation, computer-aided design, and software verification. In many cases, heuristic methods are the key to solving these problems effectively. However, the design of effective heuristic algorithms, particularly algorithms for solving computationally hard problems, is a difficult task that requires considerable expertise.

Engineers use mathematical models to describe the production of plastics and other chemicals. The models contain unknown parameters that are estimated from plant data. In the past year, the research team analyzed several criteria that modelers use to decide how complex or how simplified their models should be. They showed that one popular model-selection criterion, the corrected Akaike Information Criterion, tends to select very simple models, and that another, the adjusted coefficient of determination, tends to select models with many parameters.

The goal of this project is to evaluate if mesoscale (35 km) meteorological ensemble forecasts coupled to a short-range hydrological forecasting system can lead to improved forecasts, and thus help maximize hydropower production and minimize flood risks. Positive results would pave the way for a full project which would aim to design an efficient short-range hydrological ensemble forecasting system adapted to the climate and hydrology of the Great-Lakes and Saint Lawrence River basin.