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Computational Science Graduate Minor
The Pennsylvania State University

STUDENTS MUST APPLY FOR THIS MINOR EARLY!

 Timing: Official requests to add a masters minor to a student’s academic record must be submitted to Graduate Enrollment Services at least one semester prior to the semester the student intends to graduate.  Official requests to add a minor to a doctoral candidates academic record must be submitted to Graduate Enrollment Services prior to establishing the Ph.D. committee and prior to scheduling the comprehensive examination.

If you have additional questions, please contact Dr. Stewart (email: sstewart).

WHAT IS THE COMPUTATIONAL SCIENCE GRADUATE MINOR?

The minor in Computational Science provides an opportunity for graduate students in all colleges and majors to pursue a focused set of courses emphasizing the theory and application of computational science. Computational science involves the use of computers and data to both the analysis and design of systems and also the real-time execution of systems that, through onboard computation, can adapt and regulate their behavior. This Minor can be a valuable program for almost any engineering graduate student at Penn State. 

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. 

REQUIREMENTS FOR THE MINOR

In accordance with policy GCAC-641 Minor - Research Masters, this masters minor requires a total of 9 credits, with at least 50% of credits at the 500-level.  Six of the 9 credits must be in addition to the credits applied towards the major Masters program and they may also not be offered by the major department.

In accordance with policy GCAC-611 Minor - Research Doctorate (updated as of 8/15/2022), this doctoral minor requires a minimum of 15 credits at the 400, 500, or 800-level.  At least 50% of the credits must be at the 500-level.  These 15 credits may be counted toward the graduate major degree requirements.  All credits must be approved by the graduate major program and graduate minor program heads, who ensure that the courses planned/taken for the minor are in an area distinct from the student’s primary academic/research focus.   

Ph.D. students pursuing the Minor also need to have a CSCI faculty member on their Ph.D. committee.   

WHAT COURSES DO STUDENTS NEED TO TAKE ?  

3 credits: One 400 level or above course must be taken that clearly develops a solid basis in computing. The following courses automatically fulfill this requirement.  If you would like to petition for a different course to count for this requirement, please contact the program director.  
  

 

AERSP 424 

Advanced Computer Programming 

CMPSC 450 

Concurrent Scientific Programming 

NUCE 530 

Parallel/Vector Algorithms for Scientific Applications 

CSE 557 

 

Concurrent Matrix Computation 

 

3 credits: One 400 level or above course must be taken in applied math that supports computational methods. The following courses automatically fulfill this requirement.  If you would like to petition for a different course to count for this requirement, please contact the program director. 
 

 

MATH 523 

Numerical Analysis I 

 

MATH 550 / CSE 550 

Numerical Linear Algebra 

 

STAT 500 

Applied Statistics 

 

STAT 557 or IST 557 

Data Mining

 

 

3 (MS) or 9 (PhD) credits: Select additional credits from a list of approved courses maintained by the graduate minor program, noting requirements for the number of credits at the 500-level and for the number of credits earned outside the home department. 

  

 

Total credits required: 9 (MS) or 15 (PhD) 

 

 


HOW DOES A STUDENT APPLY FOR THE MINOR?

To apply for the minor, you will need to complete two forms, one for the department and one which will go on to the graduate school.  The graduate school form can be found here:

https://gradschool.psu.edu/forms-and-documents/ges-owned-forms-and-documents/addgrminorpdf/ 

 

If applying for the masters minor, please also complete the following form: http://www.csci.psu.edu/CSCI_Minor_Masters_App.pdf

 

If applying for the doctoral minor, please also complete this form: http://www.csci.psu.edu/CSCI_Minor_Doctoral_App_6_2022.pdf

 

On the form you must list the courses which you plan to take to complete the minor as per the instructions on the previous page.  If you have questions about specific courses, please contact the program director.  Once you apply and are accepted into the minor, if you make any changes to your course plans, you must submit updated forms before your final semester begins.

 

Upon completion of filling out the form, please sign it and have your graduate program director or department head sign the form, confirming that the courses which would be used to meet the minor are in an area distinct from the your  primary academic/research focus, before returning it to the address listed below. 

 

*Official requests to add a masters minor to a student’s academic record must be submitted to Graduate Enrollment Services at least one full semester prior to the semester the student intends to graduate.


**“Official requests to add a minor to a doctoral candidate's academic record must be submitted to Graduate Enrollment Services prior to establishing the Ph.D. committee and prior to scheduling the comprehensive examination.”

 

Send or deliver the completed form (with dept head/grad officer signatures) to:

Dr. Susan W. Stewart, Associate Teaching Professor, Aerospace Engineering

233E Hammond Bldg., Penn State, Univ. Park, PA 16802

sstewart@psu.edu

 

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 Dr. Stewart.  

ADDITIONAL COURSES

 

  • ABE 513, Applied Finite Element, Boundary Element, and Finite Difference Methods
  • ABE 597 / CH E 597: Synthetic Biology: Understanding, Designing, and Programming Cellular Behaviors
  • ABE/CE 597: Computational Ecohydrology

  • 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  
  • AERSP 424: Advanced Computer Programming (CORE COURSE)
  • AERSP 514: Stability of Laminar Flows
  • AERSP / ME 524: Homogeneous Turbulence
  • AERSP / ME 525: Inhomogeneous Turbulence
  • AESRP 554: Statistical Orbit Determination
  • AERSP 560: Finite Element Methods in Fluid Mechanics and Heat Transfer
  • AERSP 597E: Estimation Theory
  • AERSP 597G: Theory and Applications of Glabal Navigation Satellite Systems
  • AERSP 597: Kalman Filtering
  • AERSP 597: System Identification
  • AERSP 597: Parallel Computing (spring 2019)

  • ARCH 597A, Topics in Visualization

  • ASTRO/Physics 527, Computational Physics
  • ASTRO 515, AstroStatistics  
  • ASTRO 528, High-Performance 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 574/PNG 550: Reactive Transport in Natural Environments
  • CE 597: Computational Methods for Environmental Flows
  • CE 597: Meshfree Methods & Advanced Computational Solid Mechanics
  • C E 597B: Stochastic Structural Mechanics
  • CE/ABE 597: Computational Ecohydrology
  • CE 597: Nonlinear Structural Mechanics
  • CE 597: Computational Inelasticity
  • CE 597: Computational Modeling with OpenFOAM
  • CE 597: Meshfree Methods & Advances in Computational Sold Mechanics

  • CH E 512 (formerly 597A): Optimization in Biological Systems
  • CH E 524: Applications of Thermodynamics
  • CH E 597: Numerical methods in chemical engineering
  • 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 445W, Applied Machine Learning in Data Science
  • 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 / 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 Machine Learning
  • CSE 586 / EE 554: Computer Vision II
  • CSE 597, Applications of Parallel Computers
  • CSE 597, Implementation of Parallel Computer Codes
  • CSE 597: The Markov Chain Monte Carlo Method
  • 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/EME 521, Numerical Modeling in Energy and Geo-Environmental 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 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 557, Data Mining: Techniques and Applications (CORE COURSE)
  • IST 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: Artificial Emotional Intelligence
  • 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 553: Introduction to Approximation Theory
  • 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/ChemE 505: Atomistic scale simulation methods for engineers
  • 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 563 / EMCH 563: Nonlinear finite element methods
  • ME 597A: Grid Generation

  • METEO 526: Numerical weather prediction
  • METEO 586: Advances in numerical weather prediction
  • METEO 597: Data Assimilation
  • METEO 597: Practical Statistics for the Geosciences
  • 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
  • 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 508: Data Mining and Statistical Learning
  • STAT 511: Regression Analysis and Modeling
  • STAT 515: Stochastic Processes and Simulation
  • STAT 540: Statistical Computing
  • STAT 557: Data Mining I (CORE COURSE)
  • STAT 558: Data Mining II
  • STAT/Biol/CSE 598B: Bioinformatics II

  • Courses no longer or not recently 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
    • ABE 562 / EMCH 562: Boundary element analysis
    • AERSP 440: Introduction to Software Engineering
    • 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
    • CE 563: Evolutionary Algorithms (formerly 597)
    • EMCH 562/ABE 562: Boundary element analysis
    • FOR 555, Multispectral Remote Sensing
    • IST 516, Web Information Retrieval and Human Information Behavior
    • METEO 526: Numerical Methods for Geophysical Fluid Dynamics
    • MNG 557: Computational Geomechanics
    • STAT 508: Data Mining and Statistical Learning
    • 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 897: Introduction to Applied Statistics

 


Last modified:  [an error occurred while processing this directive]12/8/2021

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