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Course: Numerical Analysis. Large Scale Scientific Computing with Data
MTH 655 (CRN 33244)
, MTH 659 (CRN 33574), MTH 659 (CRN
40671, P/NP only).
Instructor:
Malgorzata Peszynska
Office hours for this class will be Mondays 2:00-3:00pm and Fridays 4:00-5:00pm.
Additional contact information including office hours is on
instructor's department website.
Class: MWF 11:00-11:50 STAG 213.
Course content:
The course will be organized in modules on (I) Nonlinear solvers,
(II), Iterative linear solvers, (III), Multiphysics, (IV) Working with
data including multiscale analysis. These modules (I-IV) cover
(T)heory and (A)lgorithms. In addition, students will develop
(P)rogramming skills in compilable code development, and introduction
to parallel computing.
Course Learning Outcomes:
A successful student will advance their scientific computing skills in
each of the three aspects (T) theoretical, (A) algorithmic, and (P)
programming, and in particular, will be able to
- Implement and analyze convergence of (variants of)
nonlinear solvers in R^N.
- Select, use, and demonstrate the properties of an
appropriate iterative solver for a sparse spd, saddle-point, or domain
decomposed system.
- Propose, implement and analyze an appropriate algorithm for a
selected model multiphysics problem.
- Pre-process and post-process the data and the solution to a
(I)BVP. In particular, work with multiscale data, conduct data
upscaling, downscaling, smoothing, coarsening, and classification.
- Use compiled (not interpreted) programming environments for
scientific computing.
Textbook and resources: Course notes will be provided, with a
link posted on CANVAS. The subject of class is vast and quickly
changing: I will use a large number of sources, with bibliography
and links to online resources available in course notes. Students
will be expected to read class notes ahead of the lectures.
Prerequisites:
Students should have solid background in differential equations and
linear algebra. Material on finite differences and other basics will
be developed as needed, but those unfamiliar with but interested in
details, e.g., on accuracy and stability, will need to supplement on
their own.
Assignments will address the Theory-Algorithms (T-A) and (P)
Programming aspects; see Schedule page and
CANVAS for due dates. The (T-A) assignments will be collected in class
on paper, and (P) assignments as journal entries, surveys, and similar
on CANVAS. No late HW will be accepted. The Announcements on CANVAS
describe the expectations on the format of the assignments.
Additional practice problems included in course notes as (Pbm) will be
discussed in class meetings marked as Discussion days.
Coding: For the (T-A) assignments, students can work in a
language and/or environment of their choice; code submission will NOT
be required.
(P)rogramming assignments will require work in compilable
environments. Students can develop code from scratch. An ability to
"translate" a template provided in one programming language to
another, and/or to correct mistakes, and/or debug own code, will be
developed. Students will report on their learning in journal entries
on CANVAS.
Grades: LO I-IV (total 80pts=4 x 20pts) will be
assessed by HW projects, each worth 20 points. Extra credit up to
20pts can be earned by participation in class discussions scheduled
after each module I-IV. To earn credit, students will be expected to
present work towards the solution of problems selected by the
instructor.
LO V (total 20pts) will be assessed via completing
surveys and journal entries as posted in Canvas. There will be
(P)rogramming projects assigned for each module I-IV. Extra credit up
to 10pts can be assigned for participation in programming
discussions and presentations.
Advanced students can discuss a possibility to substitute some
T-A or P projects by a closely related project accompanied by a class
presentation.
"Statement Regarding Students with Disabilities":
Accommodations for students with disabilities are determined and
approved by Disability Access Services (DAS). If you, as a student,
believe you are eligible for accommodations but have not obtained
approval please contact DAS immediately at 541-737-4098 or at
http://ds.oregonstate.edu/. DAS
notifies students and faculty members of approved academic
accommodations and coordinates implementation of those
accommodations. While not required, students and faculty members are
encouraged to discuss details of the implementation of individual
accommodations.
Student Conduct Expectations link:
http://studentlife.oregonstate.edu/code.
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