Computational engineering science (CES) has its roots in the modeling and simulation of technical systems and phenomena occurring in nature. Today, there is a trend toward an increasing intricacy of the systems being analyzed (complexity), a growing range of interacting scales which must be considered at once (multiscale), and larger numbers of interacting physical phenomena that are inseparable (multiphysics). Furthermore, there is increased demand for best-design identification of engineered systems with reduced input from human intuition (optimization). Therefore, in addition to pursue research in the classical CES
research fields, AICES sets out to advance computational engineering science in three critical areas of synthesis:
- model identification and discovery supported by model-based experimentation (MEXA),
- understanding scale interaction and scale integration,
- and optimal design and operation of engineered systems, including both the products and manufacturing processes.
These diverse objectives share a common trait, in that they are examples of broadly-defined inverse problems.
Prof. Omar Ghattas, Ph.D. - Large-Scale Bayesian Inversion with Applications to the Flow of the Antarctic Ice Sheet