MODERN TRENDS OF COMPUTATION, SIMULATION, & COMMUNICATION, AND THEIR IMPACTS ON THE PROGRESS OF

SCIENTIFIC AND ENGINEERING RESEARCH, DEVELOPMENT, AND EDUCATION

M. Bunjamin*

ABSTRAK

PERKEMBANGAN KOMPUTASI, SIMULASI, DAN KOMUNIKASI MODERN, SERTA DAMPAKNYA TERHADAP KEMAJUAN PENELITIAN, PENGEMBANGAN DAN PENDIDIKAN DALAM SAINS DAN ENJINIRING. Pengantar. Berikut adalah laporan singkat tentang perkembangan komputasi, simulasi, dan komunikasi modern dalam dasawarsa 90an, serta dampaknya terhadap kemajuan penelitian, pengembangan dan pendidikan dalam sains dan enjiniring. Uraian lengkap tentang topik raksasa ini jelas di luar kemampuan penulis, tetapi uraian singkat ini paling sedikit diharapkan dapat memberi gambaran menyeluruh tentang bidang yang di negara maju sangat dinamik ini. Setelah berpikir global dengan membaca laporan ini, kita di Indonesia perlu menentukan apa dan bagaimana kita harus bersikap dan bertindak guna menjawab tantangan era global ini. Sumber laporan ini adalah kumpulan jurnal dan buku komputasi & simulasi terbitan dasawarsa 90an. ABSTRACT

MODERN TRENDS OF COMPUTATION, SIMULATION, & COMMUNICATION, AND THEIR IMPACTS ON THE PROGRESS OF SIENTIFIC AND ENGINEERING RESEARCH, DEVELOPMENT, AND EDUCATION. A short report on the modern trends of computation, simulation, and com-munication in the 1990s is presented, along with their impacts on the progress of scientific & engineering research, development, and education. A full description of this giant issue is certainly a “mission impossible” for the author. Nevertheless, it is the author’s hope that it will at least give an overall view about what is going on in this very dynamic field in the advanced countries. After “thinking globally” thru reading this report, we should then decide on “what & how to act locally” to respond to these global trends. The main source of information reported here were the computational science & engineering journals & books issued during the 1990s as listed in the references below. COMPUTATIONAL SCIENCE = NUMERICAL ANALYSIS + VISUALIZATION + COMPUTER SCIENCE + SIMULATION

Numerical analysis and algorithms is one of the oldest branches of mathematics. One of the pioneers was Archimedes ± 200 BC who found the approximate value of π.

In 1225 Leonardo of Pisa studied the equation 020102 23 =−++ xxx , seeking its

* FMIPA – Univ. Indonesia & Univ. Nasional,

one real root, and got x = 1.368808107. Nobody knows his method, but it is a remarkable achievement for his time. Important progress of numerical analysis several hundred years ago were pioneered by Newton, Gauss, Laplace, and Bessel while studying astronomy.

Numerical analysis, plus visualization, simulation, and parts of computer science is now known as computational science, and has become a new scientific discipline from BSc up to PhD levels (CiSE #5 2000 p. 74). The main products of computational science are the many computational algorithms to meet the demand of modern science & engineering to solve the more and more difficult problems facing mankind. Even though applied mathematics has the abilties to solve many problems analytically, many more scientific & engineering problems are so complex that applied mathematics only “knows how to solve a problem, but can not do it.” One of the difficulties lies in the sheer size of the problem. For example, even though in linear algebra we learned how to solve a linear system of N equations with N unknowns with Cramer’s rule, we couldn’t use it when N > 1 million as demanded now. Therefore an algorithm, which enable us to give a series of instructions to a computer “to do the dirty job” for us in a gigantic scale, will make us “know how to solve a problem, and can do it.” One sacrifice we have to make for this computational solution is that it is only an approximation, although we can be sure that its deviation from the unknown “true value” is tolerably small. On the other hand, one big advantage of a computational solution is that it is a number, not a formula such as usually given by an analytical solution. For example, when trying to solve the Laplace equation in a cylindrical or spherical coordinates by separating its variables, the analytical “solution” found is in the form of an ∞-series involving Bessel or Legendre functions, which is still far from the solution demanded by real-life applications.

Modern computers were pioneered by Charles Babbage with his machine in 1834 and Kelvin in 1876. The Fortran I language, developed between 1954 – 1957, was the pioneer in compilers, which for the first time was successful in translating instructions from the high-level & English-like language into the machine language in binary system for machine execution. When the machine have finished executing all the instructions, it will report the results back to the high-level language for human consumption. This made the man-machine interaction run smoothly, and the development of better machines and languages nowadays make the man-machine interaction running even smoother. In turn, this smoother interaction enabled us to solve more and more complex problems.

With the advent of modern, colorful, and high resolution computer monitors with graphical user interfaces, a new branch of computation was established, namely computer graphics, which is very useful for scientific visualizations. The benefit of this is that it enabled us to output the computation results which traditionally consists of numbers only in a visualized form on the screen so that it can give an overall picture about the output. In modern computations, the quantity of the output in the

form of numbers can reach millions, which make direct overall grasp almost impossible. This is where visualizations, including animations, were very helpful and inevitable. Another benefit of the development of computer visualization is in its wide use in education at all levels, which made under-standing by the students easier. Besides, the visual display on the monitor can easily be transfered to a word-processor such as Word, so that the scientist who wish to communicate his computational achievements to others, and also the students who wish to print and submit their homeworks to the lecturer, can do so easily, beautifully, and completely.

The following diagram shows the interactions between i) computer science which is specializing in developing computer hardware & software, ii) applied mathematics which is specializing in developing analytical solutions, and iii) the science & engineering branches that need the services of computations. All of them are linked together by the computational science who specialize in algorithms, visualizations, simulations, and communications.

Application Science

And Engineering

Computational

Computer Science- Science = Applied Mathematics Hardware & Software Algorithms & Techniques

Visualization

Further explanation about the interactions between the four will be described below.

COMPUTATION IN MODERN R & D èè COMPUTER SIMULATION

In science, engineering, medical sciences, and economics / finance, computation

is used for computer simulation in their R & D, which developed rapidly since the end of WW-II. Traditionally, R & D activities consists of two disciplines, namely a) theory & hypothesis, which mostly is based on the following reasonings: causality principle, reductionistic & holistic, qualitative & quantitative, deductive & inductive, and b) collecting facts from direct / indirect observations from laboratory / field experiments. In the laboratory, the scientists are experimenting on a physical model of the system under investigation. In theory, the scientists are “experimenting” on a

mathematical model of the system under investigation, meaning to do simulation using analytical solutions. With the advent of computers after WW-II, a third discipline of R & D came out, namely c) computation & computer simulation, which is “experimenting” or simulating on a mathematical model of the system under investigation with the aid of a computer.

One way of showing the interaction of the three modern scientific activities is as follows. Scientific Research & Education is Done for the Purpose of: a) Meeting the Strong Sense of Curiosity among People b) Discovery & Understanding of God-created Nature c) Find Possibilities for Applications ó Manipulation of Nature for Human

Purposes.

Experiments on Physical ModeLS Simulations on Math. Models - Find Qualitative & Quantitatitive Cause è Effect Relationships

- Predict Systems Dynamics in Space & Time

- Find the values of natural parameters - Good Simulational Design - Realize the multivariate, noisy, and nonlinearworld

- Increase Effectiveness, Efficiency & Productivity of Research & Education

- Find internal structure and the interactions in - Understood Behavior of Different The system Systems with Similar Models -Good Experimental Design, avoid OFAT - Solve Inverse Problems in Space & Time - Prove whether hypotesis is right / wrong - Understand Nonlinear Systems & Try - Experiments for Education. to Control Chaotic Behavior

Theory / Hypothesis: - Good Experimental & Simulational Design - Find Mathematical Model between Cause è Effect - Understood Subsystem / System / Supersystem through: + Holistic and Reducsionistic Reasoning + Inductive and Deductive Reasonings + Qualitative and Quantitative Reasonings - Explain Facts from Experiments & Simulations - Find Feasibilities for Applications for Human Purposes.

FIND, OBSERVE, & UNDERSTAND GOD-CREATED NATURAL SYSTEMS, EITHER DIRECTLY OR INDIRECTLY

There is a big difference between theory and simulations in scientific research. In theory we can get analytical solutions only for relatively simple mathematical models with simple boundary & initial conditions. On the other hand, in computer simulations, people can solve relatively more complicated mathematical models, including the nonlinear ones, with relatively more complicated boundary & initial conditions. This trend is in accordance with the rising demands from modern science & engineering R & D who are expected to solve more complicated problems in many fields of human endeavors.

In contrast to the purpose of the above-mentioned scientific research, which were designed to understand an existing God-created natural systems, in engineering we want to create by designing and making a new − formerly non-existing − system according to our wishes. For example, in science we want to know how birds could fly, because they seemed to violate the law of gravity. This research results in the works of scientists such as Bernoulli, Euler, Navier-Stokes. The engineering analogy to this is, we want to design and produce aircrafts – at first starting from scratch as were done by the Wright brothers many years ago – by applying Navier-Stokes equations, and that way copying the bird’s technology. This is an example of a general phenomena where, on one hand, the birds were given the ability to fly by God. On the other hand human beings, who can not fly, have to think and work very hard, with high costs and long time, involving thousands of people, with many casualties from test-flight accidents, in order to build good aircrafts to enable them to fly.

The following diagram shows the interaction of the three modern engineering activities.

Engineering Research & Development for the Manipulation of Nature for Human Purposes.

Experiments on Physical Models Simulations on Math. Models - Apply Cause è Effect Relationship - Predict Behavior of Dynamical Systems - Use the Values of Natural Parameters - Design & Optimize New Systems - Realize the Noisy World - Test New Designs / New Materials - Realize the Multivariate Problems - Increase Efficiency & Productivity - Decide Internal Structure of Systems - Solve Inverse Problems in Space & Time - Test New Designs / New Materials - Control Chaotic Behaviors of - Test New Prototypes Nonlineart Systems

Theory / Hypothesis: - Design New Products According to Wishes thru Computer Simulations - Apply Known Mathematical Models between Cause è Effect - Understood Subsystem / System / Supersystem thru: + Holistic and Reducsionistic Reasonings + Inductive and Deductive Reasonings + Qualitative and Quantitative Reasonings - Explain Observed Facts from Prototype Testings - Optimize Production Pr ocesses - Products Should Ideally Be: + User-friendly, Energy-saving and Long-lasting + Inexpensive and Good-quality + Environmentally Friendly in Space & Time & Recyclable

COMPUTERS & SIMULATION IN MODERN SCIENTIFIC & NGINEERING EXPERIMENTS

In the previous section, one way of showing the interactions between theory, experiment, and simulation were shown. Another way of representing the interactions between the three is shown below. It is clear from this diagram that the emphasis here is on the experimental aspects. The system under study has many subsystems, and each consists of many sub-subsystems, etc. Fundamental to all systems theory is the

DESIGN, DEVELOP AND PRODUCE NEW PRODUCTS èè MAN-MADE SYSTEMS

cause-effect relation-ship between stimulus / input and response / output. This relationship may be qualitative, such as the statement that changing the temperature (= input) of a metal rod (= system) will change its length (= output), and also quantitative, which is the mathematical model of the system. In the case of the metal rod’s length as a function of its temperature, the mathe-matical model is L t = L 0

(1 + λt), which is well-known to every high school student. x = input = stimulus

u= output = response

= cause = effect

Prediction and

= subsystem

Optimization Once the mathematical model of a system is found, one possible use is for prediction.

In the case of the metal rod, if its length at 00 , namely L 0 , and its expansion-

coefficient, namely λ, were known, then the mathematical model can be used to predict its length at any temperature t, without really heating-up or cooling-down the rod. Another use of a mathe-matical model is for optimization, such as shown by the

Simulation, Prediction, & Optimization on Mathematical / Computer Models

Theory & Hypothesis

P U B L I C A T I O N

Real System or Model under Study

Mathematical Model: u = f(x)

Data Analysis & Interpretation

Experiment & Observation on Physical

Models

Experimental Design è Increase the

Efficiency & Productivity of Experiments

Noise Environmental

Condition

SS SS SS

SS SS SS

SS

SS

SS

SS

SS

following. One of the construction engineer’s task in designing a flyover bridge, for example, is to decide what is the proper thickness of the bridge’s supports. The rule is, it should neither be too thin lest the bridge should collapse, nor too thick lest the construction costs should be unnecessarily high.

There are two ways to find the mathematical relationship between inputs and outputs of the system under study, namely a) experimentally / empirically through the use of least square method, or b) theoretically, using physical laws such as the law of the conservation of mass. There are three possible uses of computers and simulation to assist experiments, namely: a) computer simulation for experimental design, b) computer for controlling the experiment & for data acquisition system, and c) computer for the analysis of experimental data. Experimental design and computer simulation

It is important to recognize that the analysis of the data must be integrated into the design of experiment from the outset. There is a branch of statistics – design of experiments / DOE – which is concerned with planning experiments so that the maximum amount of information (= output) can be extracted from an experiment from a given amount of effort (= input). Even though the roots of the DOE lie in the life-sciences, DOE methods are also very valuable in other branches of science. Computer programs can assist in the application of DOE methodology from the planning phase of the experiment up to the analysis phase. It is now a widely accepted opinion that the scientists who ignore the statistical DOE risk working at a reduced efficiency and missing the opportunity to get valuable experimental information. At this time of scarcity of funds for research, especially in developing countries, this strategy should never be ignored.

Most experimenters believe that good experimentation depends on the skill and imagination of the experimenter. This is not entirely true. Over 70 years ago Sir Ronald Fisher showed that there is an underlying mathematical structure that governs how experiments should be conducted. Scientific insight and experience are as desirable as ever to determine the measurable responses and the experimental factors and procedures. However, once all this critical information has been determined (to the degree possible), it is mathematics and not insight and creativity, that dictates the experimental structure. The power of experimental science is in its ability to actively manipulate the system under study, thereby establishing the causal link between what the experimenter gives inputs / stimuli to the system and observing what / how the system responded (= output). Choosing the best inputs so as to gain the best overall picture (= output) of the system under investigation is the main purpose of a good experimental design.

Control of Experiments & Data Acquisition System

In this case the system under investigation is linked – thru A-D and D-A converters – to a computer, for two purposes, namely: a) thousands of data, probably even millions of them, coming out of the system under study may be collected and stored automatically by the computer, and b) the computer may also be programmed to control the execution of the experiment. These belong to the Laboratory Automation System. The Analysis of Experimental Data

Once experimental data were collected and stored in the computer’s memory, it must be analyzed. The purpose of the data analysis is to extract as much scientific information as possible from the raw data. Everything done at this stage must have been foreseen and planned beforehand during the experimental design stage. Keywords: Experimental Design. Microcomputer-Based Laboratories. Laboratory Automa-tion. Virtual Instrumentation. Virtual Laboratories. Analysis of Experimental Data. COMPUTATIONAL SCIENCE èè THE TOP 10 ALGORITHMS OF THE 20TH CENTURY

The following is a list of the “Top 10 Algorithms of the 20th Century” according to CiSE, and was the main topic of its Jan-Feb. 2000 issue. We may agree or disagree with this choice, but the least we should do is not to under-estimate them.

1. Integer Relation Detection / IRD. 2. The (Dantzig) Simplex Method for Linear Programming. 3. Krylov Subspace Iteration 4. The QR Algorithm. 5. Sorting with Quicksort. 6. The Decompositional Approach to Matrix Computation.

6.1.The Cholesky Decomposition 6.2. The LU Decomposition 6.3. The QR Decomposition 6.4. The Spectral Decomposition 6.5. The Schur Decomposition

6.6. The Singular Value Decomposition 7. The Fast Fourier Transform. 8. The Metropolis Algorithm. 9. The Fortran I Compiler. 10. Fast Multipole Algorithm. COMPUTATIONAL SCIENCE – SOME OTHER ALGORITHMIC DEVELOPMENT

Old recipes found new applications: Pade Approximants, Rayleigh Quotient Iteration, Integrodifferential Equations, Richardson Method, Numerov Method, etc.

New recipes: Optimization – Simulated Annealing Genetic Algorithms – mutation, crossover, reinsertion, multiobjective optimization, computing parallelism Multigrid Methods: Solve PDEs Cellular Automata / Lattice Gas Method: Solve PDEs Wavelets: Transforms, Solve PDEs, Denoising. Nonstandard Finite Difference Method: Solve PDEs Computer Algebra Systems Multidimensional Integrals – Quasi Monte Carlo Massive Data Visualization Graphical Models for Problem Solving COMPUTATION & SIMULATION IN MODERN SCIENCE AND MEDICINE

Large-scale Sparse Linear Problems and Large-scale Eigenvalue Problems Automatic Differentiation Nonlinear Dynamics – Chaos – Fractals. Inverse Problems – Panoramas of the Seafloor, How Erotions Builds Mountains, Africa is the Birthplace of Humanity, The Earliest History of the Earth, Gamma-ray Bursts. Computational Physics: Solution of Schroedinger, Maxwell, Newton, Ising models, etc., Studying Astrophysical Thermonuclear Flashes. Computational Earth System Science: Computation in Weather Forecasts:

Computational Chemistry: Molecular Dynamics, Molecular Mechanics, Graph Theory. Computational Medicine – Diagnosis & Therapy: 3-D Knee Modeling and Biomechanical Simulation, Simulating the Immune System, Computer-aided Diagnosis, Computer-aided Surgery. Computational Biology: AceDB: A Genome Database Management System, Computational Cell Biology, Computational Challenge of Linkage Analysis: What Causes Diseases?, Whole Genome DNA Sequencing, Monte Carlo Simulation of Biological Aging. Computational Cosmology: Computing Challenges of the Cosmic Microwave Background, Fitting the Universe on a Supercom-puter, Fluids in the Universe: Adaptive Mesh Refinements in Cosmology. Simple Models of Evolution and Extinction Massive Data Visualization Pattern Formation Simulation DETERMINISTIC, STOCHASTIC, AND CHAOTIC SYSTEMS

It has always been the aim and motivating drive of scientists to discover order and natural laws (= mathematical models) in the abundance of natural phenomena. Knowing order and laws of natural systems will enable us to make predictions about the nature’s behavior, which turned out to be deterministic in character. At the root of this thinking, on which Descartes, Newton, and Laplace have left their stamp, lies the principle of reductionism, which is the believe that any system, no matter how complex it is, can be comprehended totally once we understood the functions of its subsystems / parts. One example of a deterministic system is our solar system, whose dynamics is well known to the astronomers as to enable them to predict – for instance – the occurence of an eclipse.

A second kind of natural systems found by scientists are the stochastic systems, whose future behavior is completely unpredictable. The Heisenberg uncertainty principle is one proof of the existence of such a system.

At the end of the 19th century, Poincare realized that – in contrast to the idea of Laplace – the deterministic equations of classical mechanics by no means lead to a predictable behavior only. For a long time Poincare’s idea found little response in science until the revolutionary development of computers led to an astonishing discovery by Lorentz in the 1960s that even the simplest deterministic equation containing no stochastic terms can show irregular, random behavior. A prerequisite, however, is that the process be nonlinear. Therefore, a third kind of natural systems were found by scientists, namely the chaotic systems.

As a consequence, the subjects which had eluded scientists for a long time were again of burning interests, first and foremost the unsolved problem of turbulence, and the predictability of the weather and the earth-quakes. In all branches of science such as biology, physics, chemistry, mathematics, astronomy, and ecology, inspired activity began and a new field of science came into being, the nonlinear dynamical systems. New books and journals on this topic came out to stress the importance of this new field. Also the setting up of special research centers such as Colorado Center for Chaos and Complexity, Santa Fe Institute Studies in the Sciences of Complexity, Center for Nonlinear Studies – Los Alamos National Laboratory, etc.

The basic phenomena of nonlinear systems, among others, are the following. a. Nonlinear deterministic systems can have regular, but also completely irregular

behavior. The slightest disturbance in the initial condition can lead to totally different temporal responses, making long-term prediction impossible. This is chaos.

b. Nonlinear deterministic systems can change its behavior. A static state of equilibrium can change to a periodic behavior thru bifurcation, period doubling, quasi-periodic behavior, or even chaotic behavior.

c. Scale invariance and self-similarity – fractals. d. Chaos may be controlled. COMPUTATION & SIMULATION IN MODERN ENGINEERING: SOLVE INVERSE PROBLEMS

A system is defined to be an entity consisting of many cooperating and interacting parts (= subsystems), with clearly definable boundaries, capable of being studied by means of measurements, and that react to external stimuli / input in a known predictable manner / response / output. Fundamental to all systems theory is the cause è effect relationship between input / stimulus and output / response. From the system’s point of view, our scientific & engineering problems may be classified as follows. Out of the three main parts of a system, namely input, system, and output, a scientific & engineering problem we face will be: two of the three are given (= !), and the research we do is supposed to find the unknown third (= ?). Therefore, depending on which two are given and which one is to be found thru scientific & engineering research, there are three possibilities, as follows.

Analysis Problems ó Forward- or Direct Problems

Synthesis or Modeling or Design Problems ó Inverse Problems

Control or Instrumentation Problems ó Inverse Problems In an analysis problem, the input and the system are given, and the response /

output is to be determined through research. This belongs to a Forward- or Direct Problem. This is typical of many (trial & error) experiments, where the experimenter gives a certain input to the system under study and then observe the outcome / response of the system.

In a synthesis or modeling or design problem, the input and output are given, and the system that convert the given input to the given output is to be determined through research. This belongs to an Inverse Problem. This is a typical problem facing the industry who wants to produce a certain product with certain specifications / properties = output when the system is receiving a certain input. To give a concrete example, suppose Ford Motor Co. is planning to build a car (= unknown system) with the property that when it accidentally hit a wall at 60 km/h speed (= given input), the driver must survive (= given output). The problem is how to build the car with the required properties. That is why this group of problems are called synthesis or design problems. One thing to remember is, this is a design problem only during the design until the construction of a prototype. Once the prototype is finished, it must be tested physically, namely, the car is physically driven at 60 km/h and intentionally being hit to a wall. This is called destructive testing, because the new prototype is really destructed in order to see whether the driver (usually a dummy) “survived” or not. Therefore this experiment belongs to the analysis / direct problem.

It is also called a modeling problem if the scientists wanted to know the mathe-matical model of the system under study, either empirically or theoretically.

In a control or instrumentation problem, the system and the output are given, and the input that produces the given output is to be determined through research. This also belongs to an Inverse Problem. As an example of this kind of problem, suppose the manager of a cosmetics industry wanted to produce a facial cream (= unknown input) that can make a lady’s face (= given system) whiter (= given output) than before using the cream. Once the prototype cream is finished, it must be tested physically, namely, the cream is physically applied to a lady’s face to see (after some

Input = ! Output = ? System = !

Input = ! Output = ! System = ?

Input = ? Output = ! System = !

time) whether or not the cream really whiten the face. Again, this physical experiment belongs to the analysis / direct problem.

The following list contains part of the very big field of computation & computer simulation applications in the modern industries in the USA. Experience in the 1990s proved that the adoption of the methods of computation & computer simulation in the industry can reduce R & D costs, testing costs, and production costs, resulting in better quality products (usually meeting ISO standards) at reduced and affordable prices. This is really a good news for the consumers, but bad news for the competing industries, and also bad news for developing countries who are still sticking with traditional methods of manufacturing processes.

The following is part of the long list of engineering applications of computation. Computational Fluid Dynamics / Turbulence Simulation Computational Electromagnetics in the Time Domain Computational Heat Transfer Computational Nuclear Reactor Analysis Computational Materials Science Computational Chemical Engineering Computation in Communication: Fast Computational Techniques in Indoor Radio Channel Estimation, Modeling the Global Internet, Wavelets in Optical Communications, Satellite-Constellation Design. Computation for Optimization Computation in Rocketry and Space Engineering Computation in Structural Engineering, Dynamic Fracture Analysis: Fracture and Damage at a Microstructural Scale, Large-scale Atomistic Simulation of Dynamic Fracture, Molecular Dynamics of Cracks, Scalable Molecular Dynamics for Material Simulations. Simulating Accidental Fires and Explosions Vehicular Traffic Simulations, Vehicular Crash Simulations Virtual Test Facility for Simulating the Dynamic Response of Materials A Multiple-Choice Genetic Algorithm for a Nonlinear Cutting Stock Problem Predicting New Materials. COMPUTATION & SIMULATION IN MODERN ECONOMICS AND FINANCE

Reading recent books such as written by Simon & Blume on Mathematics for

Economists and Chiang on Dynamic Optimization, told us about how sophisticated the kind of mathe-matics which is now needed by the modern economists. The central issue in these books are optimization, including constrained & unconstrained, linear &

nonlinear, as well as static & dynamic optimization problems. These facts show how modern economics is becoming more and more quantitative, at the same time moving toward more and more efficient operations. In the field of modern finance, they even have a new branch of knowledge called Computational Finance. One important model used here is the Black & Scholes model used to help predicting the fluctuation of stock prices in the market. The finite differ-ence methods for solving boundary- & initial value problems such as Crank-Nicholson method is now a standard procedure in Computational Finance. COMPUTATION & SIMULATION IN MODERN HIGHER EDUCATION SYSTEM

The above-mentioned description of the dynamics of modern R & D also holds in modern science & engineering education system. In this case the science students are studying to understand God-created natural systems, while the engineering students are learning how to design & create man-made / artificial systems. The impact of the dynamics of R & D on the education system may be felt from the saying: “the research of today is the education of tomorrow.” For example, the QR algorithm was for the first time reported by Francis in 1961 / 1962, but it is already included in the “Algebraic Eigenvalue Problem” book by Wilkinson in 1965. Conversely, the product of today’s education, namely the young scientists & engineers, will be the research workers of tomorrow. Therefore, a close cooperation between research óó education is a must if we want to make real progress.

The two main issues in education are what to teach and how to teach. The what to teach part of the issue is important because we can not afford to teach the students something which later turned out to be useless in solving the society’s problems. In CiSE #5 2000 p. 74 it was reported that the State University of New York (SUNY) at Brockport has a new Department of Computational Science educating students from undergraduate up to PhD levels. This fact proved that computational science answers at least part of the question: what to teach, because of the need to meet the demand of a modern society.

The how to teach part of the issue is just as important because we can not afford to teach the students without worrying whether they understood it or not. Part of the effort to decide on how to teach is how to motivate the students on the importance of the subject they are studying. A new teaching method which is now popular in the US is the Student-Centered Learning / SCL, where students are active learners, in contrast to the traditional Lecturer-Centered Learning where students were only passive listeners & spectators. It turned out that computational science is again playing an important role in SCL, as repor-ted by W.E. Boyce et al. in their report: “Interactive

Multimedia Modules in Mathematics, Engineering, and Science,” Computers in Physics, Vol. 11, No. 2, Mar/Apr, 1997.

In connection with the Indonesian condition, there are two other things which are strongly related to the student’s motivation to study science & engineering seriously.

First, for a country like Indonesia whose people are mostly moslems, those who are studying science should realize that what they are doing is not against God’s will. In fact, it is even implicitly instructed by God thru the Quran. Hence every good moslem should work & think very hard to study science, meaning to pay attention to God’s signs.

Second, the Indonesian government should stop, or at least reduce, importing finished goods that we can produce ourselves for local consumption, or stop importing luxurious things (cars, furniture) that we can afford to live without. By reducing imports and pushing local industries to progress and expand, we are creating many job openings for our fresh graduates. Please remember that it is a waste of effort, money, and time trying to raise the quality of our higher education system if, after graduation and leave the campus, the fresh graduates have nowhere to work and thereby adding the already long list of unemployment. Of course, ideally, the fresh graduates should be able to create jobs rather than just waiting for job openings, but without special trainings, this is easier said than done. Therefore in many campuses in the US, the students are getting the needed extra trainings to get acquainted with business opportunities and to get the needed field experience before he / she can manage his / her own business. Key-words in modern education system

Distance Learning, Virtual Class-rooms, Virtual Laboratories, Virtual Libraries. Large lecture classes place the students in the role of passive spectators at a performance, rather than as active participants in an educational experience. To hear is to forget, To see is to remember, To do is to understand. To hear is to wonder, To see is to follow, To do is to understand. Thinking and doing are better than watching and listening. Student-Centered Learning.

IMPACTS OF MODERN COMMUNICATION SYSTEM ON THE PROGRESS OF RESEARCH & DEVELOPMENT & EDUCATION

Another technological progress in the 1990s with great impact on the progress of scientific & engineering research, development and education system is the emergence of local, regional, and global communication system thru computers & Internet. This communica-tion system has enabled the scientists and engineers to cooperate and synergize very effectively and efficiently, even when they are physically hundreds or even thousands of kms apart from each other. Also, where expensive experimental research were done in one location, or complex simulation using expensive parallel machines were executed in one place, many scientists from different places can take part remotely. High-performance parallel computers which now are widely used in the US are also the product of computer communication technology.

The same development were happening on many campuses, where the students may take part in many scientific activities, even when they are staying at home. All they need is a laptop computer equipped with the needed commu-nication facilities, and a telephone. They can also read library books and journals from home any time they like (virtual library), and also can do many “scientific experiments” from home (virtual labora-tories). Therefore, many campus activities can now be done from home. Of course, the togetherness in discussions, seminars, workshops, etc. on campus is still needed by the students and lecturers from time to time, because human beings are basically social beings who need personal contacts no computer and Internet can replace. The point is, however, being separated physically does not mean that they are isolated scientifically.

Talking about communication, two things come up to our mind, namely what to communicate and how to communicate between a communicator and a communicant. The what part means that the communicator should know precisely the content of the message to be communicated. The use of computers discussed in this report is tilted heavily toward the what part of communication. The how part is the method of communicating, whether face-to-face or remotely using a telephone, facsimile, e-mail, or others. HIGH-PERFORMANCE COMPUTATION AND SCIENTIFIC PROGRAMMING LANGUAGE Hardware: Systems consisting of tens / hundreds / thousands computers in parallel,

each were equipped with hundreds of MByte memory and processors

with Teraflops speed, and central harddisk with hundreds of Gbytes capacities.

Languages: C, C++, Fortran 90, Power Fortran, cT, Forth, Python, Java, Ada, IDL, Parallel / OOL, Computer-Algebra Systems: Maple, Mathematica, Macsyma.

Operating Systems: DOS, Windows, Unix, Linux. WHAT HAPPENED IN THE 1990S èè WHAT MAY HAPPEN IN THE 21ST CENTURY The Death of Disciplines. Biologically Inspired Computing. Advancing Simulational Science and Engineering at Disciplinary Interfaces Quantum Computing. ASCI – Accelerated Strategic Computing Initiative in the US. One of the ASCI program’s major goals is to create the leading-edge computational & simulational capabilities necessary to shift from experiment-test-based è simulation-test-based methods. COMPUTATION & SIMULATION óó OPPORTUNITY FOR POOR COUNTRIES TO PROGRESS

The biggest problems facing the poor & developing nations in the world while

entering the 21st century is: how to keep up with the progress of advanced nations, or at least not to be left too far behind, in science & engineering development. In reality, the efforts done by the developing nations to chase the advanced countries is more or less futile. This is due to the fact that: i) the advanced countries themselves are advancing at a very, very fast pace, mainly due to their intensive & extensive use of computer simulations, ii) they keep many important information secret so that they can monopolize the world market, iii) the progress of most developing countries are very, very slow, mainly due to their ignorance of computer simulations, iv) most developing countries have no sense of danger / no sense of priority / no sense of urgency. Therefore the choices left for the developing nations are:

a) do nothing & passively wait for the fate in this era of global competition, and

leave our future to IMF / WB / ADB / CGI / UN while going on importing CBU luxurious cars, or

b) Think Globally è Act Locally: Be Proactive and Work Hard: a) to stop wast-ing our natural resources and destructing our environment, b) to develop human resources by educating students at universities using computer simulations, c) to do research which is mostly computer-based simulations and to a lesser extent laboratory-based experiments, d) to assist government institutions and private sector to solve their scientific & engineer-ing problems thru computer simulations, e) to assist high school science teachers to help them raise science teaching standards at their schools, f) our government should stop, or at least reduce, importing finished consumer goods, motor cycles, cars, etc. so that local industries may develop and progress, thereby creating more job openings for our university graduates, g) the students themselves must also work & think very hard in order to reach the highest possible knowledge and competence to enable them to face the intense global competition, including finding job opportunities overseas.

The feasibilities of these ideas are due to the facts that:

a) Experiments in laboratories are expensive, but the costs can be reduced through good experimental designs, especially with the help of computer simulations, such as using Monte Carlo method (see Bevington & Robinson),

b) Computer simulations are much cheaper than experiments, due to the facts that computer hardwares & softwares are getting more & more sophisticated while the prices are getting lower & lower and within reach of the buying powers of many people.

c) Of course, using only traditional methods of R & D without computer assistance, we still can make progress, even though our rate of progress will be very, very slow indeed, and catching up other nations who are using computers is certainly out-of-question.

d) The decade of the 1990s proved that the US was playing the role of a trend-setter of the world in science & engineering, especially through computation & simulation. On the other hand, for a developing nation like Indonesia, experience proved that even to be a trend-follower is not easy, if not impossible. Therefore, the question for Indonesia is not how to catch up with the US, but how not to be left too far behind by our neighbors such as Malaysia and Singapore. If even competing against our neighbors is difficult, then we are really a hopeless nation.

e) Basically, if we really want to make progress at a good pace in science, engineering, and education, all the capabilities of a computer system to collect, store, access, compute / process, display, and communicate data at a fantastic speed should be fully utilized.

CONCLUSIONS ON MODERN SCIENCE, ENGINEERING & EDUCATION:

Theory: Know how to solve a problem, but can not do it. Experiment: Know how to solve a problem and can do it, but expensively and slowly. Computer Simulation: Know how to solve a problem, and can do it cheaply and quickly.

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