Mission and Vision

Our Mission :

Our mission is to expose future scientists and engineers to the simulation scientific method of problem identification, modeling, simulation and evaluation. Exposure will be gained through a combination of graduate course work, which spans the simulation science pipeline and individual student involvement in computational engineering and science research efforts.

Our Vision:

The Computational Engineering and Science Program at the University of Utah trains students to perform cutting edge research, which spans the simulation science pipeline. Students will be able to identify and advance the simulation science pipeline within computational engineering and science endeavors, and thus will spearhead a new generation of simulation scientists prepared as interdisciplinary “bridge-builders” that facilitate interconnections between disciplines that typically do not interact.

Our Purpose:

The primary purpose of the Computational Engineering and Science program is to train students in the use of advanced computing hardware and modern computational, graphical, and mathematical techniques for the solution of problems in science and engineering that are inaccessible without such integrated expertise. Based upon this purpose, the goal of the CES program is to provide a mechanism by which a graduate can obtain integrated expertise and skills in all areas that are required for the solution of a particular problem: the realization of the problem in its engineering or scientific context, the translation of the problem into a precise mathematical statement through mathematical modeling, the formulation of the numerical methodology for solving the problem, the selection of the appropriate computer architecture and algorithms, and the effective interpretation of the results through visualization and/or statistical reduction. The M.S. degree in Computational Engineering and Science can serve as a stepping stone for students who want to pursue professional careers or continue in Ph.D. programs in computational chemistry, physics, computational medicine, bioinformatics, engineering, and many computer science disciplines including graphics, robotics, and visualization.

Simulation Science Pipeline:

Historically, the scientific method was formulated around the idea of postulating a model of natural phenomenon, making observations to validate one’s model, and correcting the model based upon discrepancies between the phenomenon and nature. Later, the scientific process was extended to include the idea of the controlled experiment. No longer was the scientist limited to passively observing the world around him to deduce the correctness of the model. This gave rise to the idea of devising controlled experiments designed to evaluate the correctness of the hypothesis in a systematic manner. This systematic process allowed the model to be updated based upon the lessons learned through the experiment. With the advent of modern computing, a new paradigm called simulation science has emerged, in which “experiment” now employed within the scientific method consists of the computational solution of the model. The simulation science scientific method consists of the following stages:

Scientific Problem of Interest (“Problem Identification”): Statement of the scientific or engineering problem of interest. Questions should be developed in such a way that quantifiable metrics for determining the level of success of the simulation science endeavor can be evaluated.

Modeling: The development of a model which abstracts the salient features of the problem of interest in such a way that exploration and evaluation of the model allows an answer to the questions specified concerning the problem of interest. Modeling techniques include, but are not limited to, deterministic or probabilistic, discrete or continuous mathematical models. Means of validating the model (determining the error introduced due to the model abstraction of the real phenomenon) should be established.

Computation: The generation of algorithms and implementations which accurately and efficiently evaluate the model over the range of data needed to answer the questions of interest. This simulation of the physical phenomenon by computational expression of the model provides the experiment upon which the simulation scientific method hinges.

Evaluation: The distillation and evaluation of the data produced through computational simulation to answer the questions of interest and to provide quantifiable determination of the success of the experiment. Methods such as, but not limited to, scientific visualization provide a means of tying the simulation results back to the problem of interest.

CES Courses and how they fit into the simulation scientific method as described above.

Science or Engineering Problem of Interest	Modeling	Computation	Evaluation
Discipline-specific CES electives Case Studies in CES	Mathematical Modeling	Adv Sci Comp I/II (SoC) or Numerical Analysis I/II (Math) or Computational Physics I/II (Physics)	Scientific Visualization