
COMPUTATIONAL SCIENCE
AN INTERDISCIPLINARY GRADUATE MINOR PROGRAM
THE PENNSYLVANIA STATE UNIVERSITY
STUDENTS MUST APPLY FOR THIS MINOR BEFORE THEIR LAST SEMESTER OF SCHOOL!
APPLY EARLY!
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
 All the students pursuing the Minor should also
make sure they "Like" the CSCI Facebook page at:
https://www.facebook.com/CSCI.Minor/
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 (M.S., M.Eng., M.A., etc.) 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
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 crosslisted 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 (M.S., M.Eng., M.A., etc.):
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 crosslisted 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 crosslisted 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
 AE 559, Computational Fluid Dynamics in Building Design
 AE 597A, Computer Modeling of Building Structures
 AE 597F, Virtual Reality Prototyping
 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 597C: Statistical Orbit Determination
 AERSP 597E: Estimation Theory
 AERSP 597G: Theory and Applications of Glabal Navigation Satellite Systems
 AERSP 597: Kalman Filtering
 AERSP 597: System Identification
 ARCH 597A, Topics in Visualization
 ASTRO/Physics 527, Computational Physics
 ASTRO 585, Astrostatistics
 ASTRO 585, HighPerformance Scientific Computing for Astrophysics
 PHYS/Biol 497: Networks in Life Science
 BIOE 597: Computational Modeling and Statistics for Bioengineering
 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
 CE 597:
Meshfree Methods & Advanced Computational Solid Mechanics
 C E 597B: Stochastic Structural Mechanics
 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 408: Computational Chemistry
 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 565: Algorithm Design and Analysis
 CSE 583 / EE 552:
Pattern Recognition  Principles and Applications
 CSE 584 (previously 598A): Machine Learning
 CSE 586 / EE 554: Computer Vision II
 CSE 597, Applications of Parallel Computers
 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 554 / CSE 586 : Computer Vision II
 EE 556,
Graphs, Algorithms, and Neural Networks
 EE 597I,
Intelligent Control
 EGEE 520,
Numerical Modeling in Energy and
GeoEnvironmental Engineering Systems
 EGEE/EME 597A,
Machine Learning for Engineering Problems
 E SCI / EE 456,
Introduction to Neural Networks
 E SCI 483,
Simulation and design of nanostructures
 E SCI 527, 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
 IE 597: Convex Optimization
 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,
Deep Learning
 IST 597,
Big Data Fundamentals
 IST 597 (also EDSGN 597, IE 597, and CSE 597) ,
Data Mining Driven Design
 IST 597,
Quantitative and Qualitative statistical methods for
HCI and Cognitive Science
 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)
 MATSE 597D (3 Credits)
Classical Molecular Modeling Methods of Polymers and Fluids
 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/ASTRO 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
 SoDA 502:
Approaches and Issues in Social Data Analytics
 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 ManyBody 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: Friday, 06Apr2018 06:27:35 EDT

