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COMPUTATIONAL SCIENCE
AN INTERDISCIPLINARY GRADUATE MINOR PROGRAM
THE PENNSYLVANIA STATE UNIVERSITY
Topics covered here:
If you plan to pursue the Minor, submit your application soon;
so we can complete the necessary paperwork. Don't wait until
your final semester to apply.
If you have additional questions, please contact
the graduate advisor in your home department or Prof. Long
(email: LNL).
WHAT IS THE COMPUTATIONAL SCIENCE GRADUATE MINOR?
The minor in Computational Science was created to
provide an opportunity for graduate students in
all colleges and majors to pursue a focused set of courses that
emphasize all aspects of computational science.
Computational science involves using computers to study
scientific problems and complements the areas of theory
and experimentation in traditional scientific investigation.
This Minor would be a valuble program for almost any graduate
student at Penn State.
Official description: The Aerospace Engineering Department administers
this interdisciplinary minor. Each student.s program is planned by the
student and a designated computational science adviser, in consultation
with the graduate adviser in the student's major field.
The minor offers an opportunity for students in all colleges and majors
to pursue a focused set of courses that emphasize computational science.
The minor requires 9 credits in computational science courses for a
masters degree and 15 credits for a doctoral minor.
All students
are required to take the two-semester (3 credits total) computational
science colloquium (AERSP 590).
All students must take six credits from:
from AERSP 424, NucE 530 (or CSE 557), MATH 523, CSE 550, Stat 500,
or Stat/IST 557.
For the M.S. minor, three additional credits will be chosen from a list
of approved courses on the Web site (www.csci.psu.edu).
For the Ph.D. minor, nine additional credits will be chosen from a list
of approved courses on the Web site (www.csci.psu.edu).
This Grad Minor was approved by the Board of Trustees on
July 14, 2006. This new Minor replaces the former
Grad Minor in High Performance Computing.
Also, on June 29, 2009 the Graduate School allowed us
to add three additional Core Courses, and remove the
requirement for AERSP 590. This change will be made
permanent in 2010.
WHAT COURSES DO STUDENTS NEED TO TAKE ?
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The COMPUTATIONAL SCIENCE Graduate Minor requires:
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Masters degree: 9 credits
in COMPUTATIONAL SCIENCE courses.
(6 credits must be from list of core courses)
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Doctoral degree: 15 credits
in COMPUTATIONAL SCIENCE courses.
(6 credits must be from list of core courses)
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NOTE: These do not have to be additional credits beyond your
graduate degree requirements. When appropriate, the same course can be
used to satisfy your graduate degree and the COMPUTATIONAL SCIENCE Minor.
(Note: you cannot
get an M.S. Minor with a Ph.D. If you are pursuing a Ph.D. major
then you have to do the Ph.D. Minor)
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WHAT ARE THE CORE COURSES ?
WHAT OTHER COURSES CAN BE USED FOR THE MINOR ?
To find out when a course is offered, please consult the
Penn State Schedule of courses .
To suggest additional courses be added to this list, please send the syllabus
and outline (or a weblink) to
Prof. Long
(LNL)
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ADDITIONAL COURSES
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- ABE 562 / EMCH 562: Boundary element analysis
- ACS 597: Computational acoustics
- AERSP 423: Intro. to Computational Fluid Dynamics
(including AERSP 596 from Spring 2007)
- AERSP 424:
Advanced Computer Programming
(CORE COURSE)
- AERSP 440:
Introduction to Software Engineering
- AERSP 514: Stability of Laminar Flows
- AERSP / ME 524:
Homogeneous Turbulence
- AERSP / ME 525:
Inhomogeneous Turbulence
- AERSP / ME 526: Computational methods for shear layers
- AERSP / ME 527: Computational methods in transonic flow
- AERSP / ME 528: Computational methods for recirculating flows
- AERSP 529: Advanced analysis and computation of turbomachinery flows
- AERSP 560: Finite Element Methods
- AERSP 590:
Computational Science Tools
(Fall semester, 2 credits)
- AERSP 590:
Computational Science Invited Lectures
(Spring semester, 1 credit)
- ARCH 597A, Topics in Visualization
- CE 541: Structural Analysis
- CE 563:
Evolutionary Algorithms (formerly 597)
- CH E 597: Numerical methods in chemical engineering
- CH E 597A:
Optimization in Biological Systems
- CHEM 560: Quantum mechanical electronic structure calculations
- CHEM 560A, Computer Simulations for Physical Scientists
- CHEM 597B, Introduction to Computational Science and Engineering
- CSE 418, Computer Graphics
- CmpSc 450, Concurrent Scientific Computing
- CSE 511 Operating Systems Design
- CSE 514. Computer Networks
- CSE 530: Computer architecture
- CSE 531: Parallel processors and processing
- CSE 532: Multiprocessor architecture
- CSE 543: Interconnection networks in highly parallel computers
- CSE 550, Numerical Linear Algebra
(CORE COURSE)
- CSE / MATH 551: Numerical solution of ordinary differential equations
- CSE / MATH 552: Numerical solution of partial differential equations
- CSE / MATH 555: Numerical optimization techniques
- CSE / MATH 556: Finite element methods
- CSE 557:
Concurrent Matrix Computation
(CORE COURSE)
- CSE 583 / EE 552:
Pattern Recognition - Principles and Applications
- CSE 598,
Advanced Topics in Scientific Computing
- CSE 598E / Stat 597E:
Data Mining
- EE / E SCI 456,
Introduction to Neural Networks
- EE 537: Numerical and asymptotic methods in electromagnetics
- EE 552 / CSE 583,
Pattern Recognition - Principles and Applications
- EE 556,
Graphs, Algorithms, and Neural Networks
- EE 597I,
Intelligent Control
- EGEE 520,
Numerical Modeling in Energy and
Geo-Environmental Engineering Systems
- E SCI / EE 456,
Introduction to Neural Networks
- E SCI 483,
Simulation and design of nanostructures
- E SCI 497B, Brain Computer Interfaces
- EMCH 560: Finite element methods
- EMCH 562/ABE 562: Boundary element analysis
- EMCH 563/ME 563: Nonlinear finite element methods
- Geo Sci 561: Mathematical Modeling in the Geosciences
- IE 522: Discrete Event Systems Simulation
- E 562: Expert System Design in Industrial Engineering
- IE 567:
Distributed Systems and Control
- IE 578: Using simulation models for design
- IE 582: Advanced Information Technology for Industrial and Manufacturing Engineering
- IST/Stat 557,
Data Mining I (CORE COURSE)
- IST 597C,
Advanced Topics in Databases
- IST 597F,
Simulating Human Behavior
- MATH 523: Computational Math
- MATH / CSE 550: Numerical linear algebra
- MATH / CSE 551: Numerical solution of ordinary differential
equations
- MATH / CSE 552: Numerical solution of partial differential
equations
- MATH / CSE 555: Numerical optimization techniques
- MATH / CSE 556: Finite element methods
- MATH 580: Applied Math I
- Math 597B, Intro to Multigrid and Domain Decomposition
- MatSC 580, Computational Thermodynamics (formerly MatSE 597C)
- MatSE 581,
Computational Materials Science II: Continuum,
Mesoscale Simulations (formerly MatSE 597)
- MatSE 597F, Polymeric Materials: Introduction to
Computational Materials Science
- ME 523 (formerly ME 540):
Numerical solutions applied to heat transfer and fluid mechanics
- ME / AERSP 524:
Homogeneous Turbulence
- ME / AERSP 525:
Inhomogeneous Turbulence
- ME / AERSP 526: Computational methods for shear layers
- ME / AERSP 527: Computational methods in transonic flow
- ME / AERSP 528: Computational methods for recirculating flows
- ME 563 / EMCH 563: Nonlinear finite element methods
- ME 597A: Grid Generation
- METEO 526: Numerical weather prediction
- METEO 586: Advances in numerical weather prediction
- MNG 557: Computational Geomechanics
- NucE 521, Neutron Transport Theory
- NucE 525, Introduction to Monte Carlo Methods
- NucE 530: Parallel/Vector Algorithms for Scientific Applications
(CORE COURSE)
- PHYS 527: Computational physics
- PHYS 597: Computational physics II
- PHYS 597B: Computer Simulation of Materials
- PHYS 597B, Introduction to Computational Science and Engineering
- PHYS 597, Graphs and networks in systems biology
- PHYS/AERSP/CHEM/CSE/MATH 597,
Introduction to Many-Body Problems and Algorithms
(no longer offered)
- PNG 511: Numerical Solution of the Partial Differential
Equations of Flow in Porous Media
- PNG 512: Numerical Reservoir Simulation
- Stat 440:
Statistical Computing
- Stat 500:
Applied Statistics
(CORE COURSE)
- Stat 501:
Regression Methods
- Stat 504:
Analysis of Discrete Data
- Stat 515:
Stochastic Processes and Simulation
- Stat 540:
Statistical Computing
- Stat 557 (formerly 597E/CSE 598E):
Data Mining
- Stat 597A:
Stochastic Dynamics of the Living Cell
(Spring 2009)
- Stat/Biol/CSE 598B: Bioinformatics II
- Stat 897:
Introduction to Applied Statistics
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HOW DOES A STUDENT APPLY FOR THE MINOR?
- Students can apply online
by filling out this form.
- Students should sign up for the Minor as soon
as possible. There is a fair amount of
paperwork, so don't delay applying.
- After the online submission, we will
write a letter to the graduate school
informing them of your intent to pursue the Minor
(once this is done, there will be a "Yes"
in the appropriate column in the
List of Students)
- When we are notified that the student
has been aproved by the Grad School,
there will be a "Yes"
in the final column in the
List of Students
- Once the student has been approved, a note
to this effect will appear on their transcript
(this must appear on the transcript before
graduation, you need to make sure of this!)
- Ph.D. students pursuing the Minor also need
to have a
CSCI faculty member on their
Ph.D. committee (and this person cannot be the
Chair of the committee)
- All the students pursuing the Minor should also
make sure they are on the CSci-GRAD-MINOR listserver,
which is how we communicate with all the students.
Instructions for joining the list are at:
LIST SERV LINK .
Maintained by:
Prof. Lyle N. Long ,
The Pennsylvania State University
Last modified: Saturday, 26-Sep-2009 19:47:43 EDT
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