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Whether you're simulating the electronic structure of a carbon nanotube or the strain within an automobile part, the
calculations usually boil down to a simple matrix equation,
Ax = f. The faster you can fill the
matrix A with the coefficients for your partial
differential equation (PDE), and the faster you can solve for
the vector x given a forcing function f, the faster you have your overall solution. Things get interesting when the matrix A is too large to fit in the memory available on one machine, or when the coefficients in A cause the matrix to be ill-conditioned.
Ax = f
Many different algorithms have been developed to map a PDE onto a matrix, to pre-condition the matrix to a better form, and to solve the matrix with blinding speed. Different algorithms usually exploit some property of the matrix, such as symmetry, to reduce either memory requirements or solution speed or both.
Learn more about algorithms from the many resources on this site, listed below.
2004 Computational Materials Science Summer School
29 Aug 2005 | Workshops
This short course will explore a range of computational approaches relevant for nanotechnology.
A Primer on Semiconductor Device Simulation
4.5 out of 5 stars
23 Jan 2006 | Online Presentations | Contributor(s): Mark Lundstrom
Computer simulation is now an essential tool for the research and development of semiconductor processes and devices, but to use a simulation
tool intelligently, one must know what's "under the...
A Scalable Algorithm for Inverse Medium Problems with Multiple Sources
04 Feb 2016 | Online Presentations | Contributor(s): Keith Kelly
We consider the problem of acoustic scattering as described by the free-space, time-harmonic scalar wave equation given by (0.1) along with radiation boundary conditions. Here, is a...
An Introduction to Quantum Computing
5.0 out of 5 stars
12 Sep 2008 | Online Presentations | Contributor(s): Edward Gerjuoy
Quantum mechanics, as formulated more than 80 years ago by Schrodinger, Heisenberg, Dirac and other greats, is a wholly sufficient foundation for its modern interrelated subfields of quantum...
Autonomic Adaptation of Virtual Distributed Environments in a Multi-Domain Infrastructure
0.0 out of 5 stars
11 Jul 2006 | Online Presentations | Contributor(s): Ryan Riley, Dongyan Xu
By federating resources from multiple domains, a shared infrastructure provides aggregated computation resources to a large number of users. With rapid advances in virtualization technologies, we...
Bandstructure in Nanoelectronics
01 Nov 2005 | Online Presentations | Contributor(s): Gerhard Klimeck
This presentation will highlight, for nanoelectronic device examples, how the effective mass approximation breaks down and why the quantum mechanical nature of the atomically resolved material...
BNC Annual Research Review: An Introduction to PRISM and MEMS Simulation
04 Jun 2008 | Online Presentations | Contributor(s): Jayathi Murthy
This presentation is part of a collection of presentations describing the projects, people, and capabilities enhanced by research performed in the Birck Center, and a look at plans for the...
Calculating Resonances Using a Complex Absorbing Potential
14 Mar 2008 | Online Presentations | Contributor(s): Robin Santra
The Siegert (or Gamow) wave function associated with a resonance state is exponentially divergent at large distances from the scattering target. A complex absorbing potential (CAP) provides a...
Challenges and Strategies for High End Computing
20 Dec 2007 | Online Presentations | Contributor(s): Katherine A. Yelick
This presentation was one of 13 presentations in the one-day forum,
"Excellence in Computer Simulation," which brought together a broad
set of experts to reflect on the future of...
Computational Mathematics: Role, Impact, Challenges
20 Dec 2007 | Online Presentations | Contributor(s): Juan C. Meza
Computational Methods for NEMS
20 Aug 2005 | Online Presentations | Contributor(s): Narayan Aluru
Computing Research Institute Seminars
08 Jan 2007 | Series
CRI sponsors a regular seminar series that features local, national and international speakers who are recognized in their fields. CRI seminars cover topics in computational science, computational...
Computing the Horribleness of Soft Condensed Matter
19 Oct 2007 | Online Presentations | Contributor(s): Eric Jakobsson
A great triumph of computer simulations 40 years ago was to make the liquid state of matter understandable in terms of physical
interactions between individual molecules. Prior to the first...
Data-adaptive Filtering and the State of the Art in Image Processing
15 Apr 2013 | Online Presentations | Contributor(s): Peyman Milanfar
In this talk, I will present a practical and unified framework for understanding some common underpinnings of these methods. This leads to new insights and a broad understanding of how these...
Data-Centric Models for Multilevel Algorithms
07 Feb 2016 | Online Presentations | Contributor(s): Samuel Guiterrez
Today, computational scientists must contend with a diverse set of supercomputer architectures that are capable of exposing unprecedented levels of parallelism and complexity. Effectively placing,...
Device Physics and Simulation of Silicon Nanowire Transistors
20 May 2006 | Papers | Contributor(s): Jing Wang
As the conventional silicon metal-oxide-semiconductor field-effect transistor (MOSFET) approaches its scaling limits, many novel device structures are being extensively explored. Among them, the...
ECE 595 Course Policy - Spring 2013
03 Jan 2013 | Teaching Materials | Contributor(s): Peter Bermel
A description of the key policies that will govern the administration of ECE 595 on "Numerical Methods" in Spring 2013.
ECE 595E Lecture 35: MEEP Tutorial I
18 Apr 2013 | Online Presentations | Contributor(s): Peter Bermel
ECE 595E Lecture 36: MEEP Tutorial II
30 Apr 2013 | Online Presentations | Contributor(s): Peter Bermel
Recap from Monday
Multimode ring resonators
Isolating individual resonances
Quantifying third-harmonic generation
ECE 695NS Lecture 2: Computability and NP-hardness
13 Jan 2017 | Online Presentations | Contributor(s): Peter Bermel
Polynomial Time (Class P)
Non-deterministic Turing machines