It is necessary to have a holistic approach to understand and re-use or engineer complex natural phenomena. Integration of different sciences with use of modeling and simulation based methods is taking increased responsibility in understanding such phenomena. Rapid increasment of computational powers gave rise an emerging interdisciplinary field of predictive sciences. Application of domain specific knowledge and predictive powers of simulations made possibility to predict, validate and engineer the response of complex systems. Such ability helps to use more “personalized” access to many different problems.
Simulation and design of new and complex systems in a variety of applications, that not only include biological systems, healthcare, material technology, climate forecasting, new energy technologies, is needed to be done effectively. Such complex systems require integration of diverse disciplines and sophisticated methods of multi-scale, mutli-model and multi-compartmental simulations. There is a need for discovering, developing and teaching common principles, techniques and methods creating common basis of understanding. The future success requires common computational tools, algorithms and bridging frameworks for integrating models, computations and knowledge/idea sharing together.
Interdisciplinary leading edge education in predictive sciences are a critical part of the development of this field. Such education do not fit well into standard educational practices in the physical sciences, the life sciences or mathematics. Teachers, scientist and students from different scientific areas are needed to meet, communicate and share their knowledge and ideas to create community that will support predictive sciences and their integration into the current one.