Science and engineering are undergoing a major transformation at the research as well as at the development and technology level. The modern scientist and engineer spends more and more time in front of a laptop, a workstation or a parallel supercomputer and less and less time in the physical laboratory or in the workshop. The virtual wind tunnel and the virtual biology lab are not a thing of the future, they are here! The old approach of ``cut-and-try'' is being replaced by ``simulate-and-analyze'' in several key technological areas such as aerospace applications, synthesis of new materials, design of new drugs, chip processing and microfabrication, etc. The methods of scientific analysis and engineering design are changing continuously affecting both our approach to the phenomena that we study as well as the range of applications that we address.

In the classical scientific approach, the physical system is first simplified and set in a form that suggests what type of phenomena and processes may be important, and correspondingly what experiments are to be conducted. In the absence of any known-type governing equations, dimensional inter-dependence between physical parameters can guide laboratory experiments in identifying key parametric studies. The database produced in the laboratory is then used to construct a simplified ``engineering'' model which after field-test validation will be used in other research, product development, design, and possibly lead to new technological applications. This approach has been used almost invariably in every scientific discipline, i.e. engineering, physics, chemistry, biology, etc.

The simulation approach follows a parallel path but with some significant differences. First, the phase of the physical model analysis is more elaborate: The physical system is cast in a form governed by a set of partial differential equations, which represent continuum approximations to microscopic models. Such approximations are not possible for all systems and sometimes the microscopic model should be used directly. Second, the laboratory experiment is replaced by simulation, i.e. a numerical experiment based on a discrete model. Such a model may represent a discrete approximation of the continuum partial differential equations or it may simply represent a statistical representation of the microscopic model. Finite differences on a grid is an example of the first case, and Monte Carlo methods is an example of the second case. In either case, these algorithms have then to be converted to software using an appropriate computer language, debugged, and run on a workstation or a parallel supercomputer. The output is usually a large number of files of a few Megabytes to Gigabytes, especially large for simulations of time-dependent phenomena. This numerical data base to be useful needs to be put into graphical form using various visualization tools, which may not always be suited for the particular application considered. Visualization can be especially useful during the simulation where interactivity is required as the grid may be changing or the number of molecules may be increasing.

The simulation approach has already been followed by many researchers across disciplines in the last couple of decades. The question that we address here is if this is a new science, and how one could formally obtain such skills. Moreover, does this constitute fundamental new knowledge or is it a ``mechanical procedure'', an ordinary skill that a chemist, a biologist or an engineer will acquire easily as part of ``training on the job'' without specific formal education. For us the University educators, has the time arrived where we need to reconsider boundaries between disciplines and prepare for the education of the future simulation scientist, an inter-disciplinary scientist?

Let us re-examine some of the requirements following the various steps in the simulation approach. The first step is to select the right representation of the physical system by making consistent assumptions in order to derive the governing equations and the associated boundary conditions. The conservation laws should be satisfied, the entropy condition should not be violated, the uncertainty principle should be honored. The second task is to develop the right algorithmic procedure to discretize the continuum model or represent the dynamics of the atomistic model. The choices are many, but which algorithm is the most accurate one, or the simplest one, or the most efficient one? These algorithms do not belong to a discipline! Finite elements, first developed by the famous mathematician Courant and re-discovered by civil engineers, have found their way in every engineering discipline, physics, geology, etc. Molecular dynamics simulations are practiced by chemists, biologists, material scientists, and others. The third task is to compute efficiently in the ever-changing world of supercomputing. How efficient the computation is translates to how realistic of a problem is solved, and therefore how useful the results can be to applications. The fourth task is to assess the accuracy of the results in cases that no direct confirmation from physical experiments is possible such as in aerospace engineering or in biosystems or in astrophysics, etc. Reliability of the predicted numerical answer is an important issue in the simulation approach as some of the answers may lead to new physics or false physics contained in the discrete model or induced by the algorithm but not derived from the physical problem. Finally, visualizing the simulated phenomenon, in most cases in three-dimensional space and in time, by employing proper computer graphics (a separate specialty on its own) completes the full simulation cycle. The rest of the steps followed are similar to the classical scientific approach.

To paraphrase Nicholas Negroponte in his recent best-seller ``Being Digital'', in classical science we are dealing with matter and therefore atoms but in simulation we are dealing with information and therefore bits, so it is atoms versus bits!. Can we then recognize the simulation scientist as a separate scientist, the same way we recognized thirty years ago the computer scientist as different than the electrical engineer? The new scientist is certainly not a computer scientist although he/she should be computer literate both in software and hardware. He/she is not a physicist although he/she needs a sound physics background. He/she is not an applied mathematician although he/she needs expertise of mathematical analysis and approximation theory.

It is expected that the market forces will respond to this new simulation science and engineering. With the rapid and simultaneous advances in software and computer technology, it is easy to predict that every future scientist and engineer will have on his/her desk an advanced simulation tool consisting of a software library and multi-processor computers that will make analysis, product development and design more optimal and cost-effective. But what the future scientist/engineer will need, first and foremost, is a solid inter-disciplinary education.

Here at Brown we have a unique opportunity to lead the way in bringing about changes in the training of this new cadre of simulation scientists and engineers. Such an education program can include both undergraduates as well as graduates but also older graduates who may want to enroll in the Simulation Science program for re-training. How exactly could this be achieved should be decided collectively by all interested parties, and such discussions have already started between faculty in Applied Mathematics, Computer Science, and Physics. However, a broader participation is required from other departments and other research programs around the University.