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Computational Science
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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).

HOW DOES A STUDENT APPLY FOR THE MINOR?

  • Students can apply by simply by filling out this form and sending it to the address on the form.
  • 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 in the Google Group for CSci Minor:
    https://groups.google.com/d/forum/csci_psu
    csci_psu@googlegroups.com
    which is how we communicate with all the students.
  • Students should sign up for the Minor as soon as possible. There is a fair amount of paperwork, so don't delay applying.



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 must take at least one of these:

    • AERSP 424, CMPSC 450, NUC E 530, or CSE 557
      (Note: AERSP 424 WILL be offered in Fall 2014)
    and at least one of these:
    • MATH 523, MATH/CSE 550, STAT 500, or STAT/IST 557.

    The additional credits will be chosen from a list of approved courses on the CSCI Web site (www.csci.psu.edu).

    In addition, for the Masters Minor and Ph.D. Minors the students can use at most 6 and 9 credits, respectively, from (or cross-listed with) their home department.

    More information can be found on the CSCI Web site: http://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 was made permanent on November 17, 2010.




WHAT COURSES DO STUDENTS NEED TO TAKE ?

The COMPUTATIONAL SCIENCE Graduate Minor requires:

Masters degree:

One of these: AERSP 424, CMPSC 450, NUC E 530, or CSE 557.
One of these: MATH 523, MATH/CSE 550, STAT 500, or STAT/IST 557.
And 3 more credits from the list of Computational Science courses.
NOTE: In addition, students can use at most 6 credits from (or cross-listed with) their home department.


Doctoral degree: 15 credits

One of these: AERSP 424, CMPSC 450, NUC E 530, or CSE 557.
One of these: MATH 523, MATH/CSE 550, STAT 500, or STAT/IST 557.
And 9 more credits from the list of Computational Science courses.
NOTE: In addition, students can use at most 9 credits from (or cross-listed with) their home department.

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)



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)

ADDITIONAL COURSES
  • ABE 513, Applied Finite Element, Boundary Element, and Finite Difference Methods
  • ABE 562 / EMCH 562: Boundary element analysis
  • ABE 597 / CH E 597: Synthetic Biology: Understanding, Designing, and Programming Cellular Behaviors

  • 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 (not offered in 2014-15 academic year)
  • 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 597C: Statistical Orbit Determination
  • AERSP 597E: Estimation Theory
  • AERSP 597G: Theory and Applications of Glabal Navigation Satellite Systems

  • ARCH 597A, Topics in Visualization

  • AE 597A, Computer Modeling of Building Structures
  • AE 597F, Virtual Reality Prototyping

  • ASTRO 585, High-Performance Scientific Computing for Astrophysics

  • PHYS/Biol 497: Networks in Life Science

  • BMMB 597D: Bio Data Analysis (ONLY 2 CREDITS)

  • CE 541: Structural Analysis
  • CE 563: Evolutionary Algorithms (formerly 597)
  • CE 597: Computational Methods for Environmental Flows

  • CH E 524: Applications of Thermodynamics
  • CH E 597: Numerical methods in chemical engineering
  • CH E 597A: Optimization in Biological Systems
  • CH E 597 / ABE 597: Synthetic Biology: Understanding, Designing, and Programming Cellular Behaviors
  • Ch E/ME 597A: Atomistic scale simulation methods for engineers

  • CHEM 560: Quantum mechanical electronic structure calculations
  • CHEM 560A, Computer Simulations for Physical Scientists

  • CSE 418, Computer Graphics
  • CmpSc 450, Concurrent Scientific Computing (CORE COURSE)
  • 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 / Math 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 598C, Meshing Techniques
  • 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 514: Data Inversion in Earth Science
  • Geo Sci 561: Mathematical Modeling in the Geosciences
  • Geo Sci 597, Practical Statistics for the Geosciences
  • Geo Sci 597, Multivariate Analysis in Geosciences

  • HDFS 597E: fMRI Data Analysis

  • 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
  • IE 597: Robust Modeling, Optimization and Computation

  • IST 516, Web Information Retrieval and Human Information Behavior
  • IST/Stat 557, Data Mining I (CORE COURSE)
  • IST/Stat 558, Data Mining II
  • IST 562, Introduction to Theoretical Foundations of Information Science
  • IST 597C, Advanced Topics in Databases
  • IST 597F, Simulating Human Behavior
  • IST 597F, Principles of Artificial Intelligence
  • IST 597, Principles of Machine Learning
  • IST 597 (also EDSGN 597, IE 597, and CSE 597) , Data Mining Driven Design

  • MATH 523: Numerical Analysis I (CORE COURSE)
  • MATH 524: Numerical Linear Algebra (formerly Numerical Analysis II)
  • MATH / CSE 550: Numerical linear algebra (CORE COURSE)
  • 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 597, Intro to Multigrid and Domain Decomposition
  • Math 597, Multiscale Modeling and Analysis
  • Math 597, Numerical Methods for Coupled Partial Differential Equations

  • MATSE 544: Computational Material Science of Soft Materials (formerly MatSE 597F)
  • MATSE 580, Computational Thermodynamics (formerly MatSE 597C)
  • MATSE 581, Computational Materials Science II: Continuum, Mesoscale Simulations (formerly MatSE 597)

  • 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
  • ME/ChemE 597A: Atomistic scale simulation methods for engineers

  • METEO 526: Numerical Methods for Geophysical Fluid Dynamics
  • METEO 526: Numerical weather prediction
  • METEO 586: Advances in numerical weather prediction
  • METEO 597: Data Assimilation
  • METEO 597: Pracical Statistics for the Geosciences

  • 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)
  • NucE 597I, Uncertainty Quantification in Scientific Computing

  • PHYS/Biol 497: Networks in Life Science
  • PHYS 527: Computational physics
  • PHYS 580: Elements of Network Science (previously 597)
  • PHYS 597: Computational physics II
  • PHYS 597: Computer Simulation of Materials
  • PHYS 597, Graphs and networks in systems biology

  • 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 505: Applied Multivariate Statistical Analysis
  • Stat 511: Regression Analysis and Modeling
  • Stat 515: Stochastic Processes and Simulation
  • Stat 540: Statistical Computing
  • Stat/IST 557 (formerly 597E/CSE 598E): Data Mining (CORE COURSE)
  • Stat 597: Data privacy methods (only 1 credit)
  • Stat 597A: Stochastic Dynamics of the Living Cell (Spring 2009)
  • Stat 597C: Introduction to Computer Environments (ONLY 1 CREDIT)
  • Stat/Biol/CSE 598B: Bioinformatics II
  • Stat 897: Introduction to Applied Statistics

  • Courses no longer offered:
    • AERSP 590: Computational Science Tools (Fall semester, 2 credits)
    • AERSP 590: Computational Science Invited Lectures (Spring semester, 1 credit)
    • CHEM 597B, Introduction to Computational Science and Engineering
    • PHYS/AERSP/CHEM/CSE/MATH 597, Introduction to Many-Body Problems and Algorithms (no longer offered)
    • PHYS 597B, Introduction to Computational Science and Engineering



Maintained by: Prof. Lyle N. Long , The Pennsylvania State University
Last modified: Tuesday, 11-Nov-2014 21:29:53 EST