## Tags: algorithms

### Description

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.

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.

### All Categories (1-20 of 146)

1. 11 Mar 2022 | | Contributor(s):: Gaby Arellano Bello

In this session, we explore the fundamentals of machine learning using MATLAB. We introduce machine learning techniques available in MATLAB to quickly explore your data, evaluate machine learning algorithms, compare the results and apply the best technique to your problem.

2. 04 Mar 2022 | | Contributor(s):: Gen Sasaki

Learn how MATLAB can be used to visualize and analyze data, perform numerical computations, and develop algorithms. Through live demonstrations and examples, you will see how MATLAB can help you become more effective in your coursework as well as in research.

3. 02 Feb 2021 | | Contributor(s):: Ning Yang, Tong Wu, Jing Guo

This folder contains two Python functions for GPU-accelerated simulation, which implements the recursive algorithm in the non-equilibrium Green’s function (NEGF) formalism. Compared to the matlab implementation [1], the GPU version allows massive parallel running over many cores on GPU...

4. 08 Jan 2021 | | Contributor(s):: Peter J. Love

I will talk about quantum simulation algorithms based on the light-front formulation of quantum field theory. They will range from ab initio simulations with nearly optimal resource scalings to VQE-inspired methods available for existing devices.

5. 28 Oct 2020 | | Contributor(s):: Samudra Dasgupta

In this talk, we lay out the systematic design considerations for using a NISQ reservoir as a computing engine. We then show how to experimentally evaluate the memory capacity of various reservoir topologies  (using IBM-Q’s Rochester device) to identify the configuration with maximum...

6. Samudra Dasgupta

Samudra Dasgupta obtained his B.Tech in Electronics and Electrical Engineering from IIT-Kharagpur 2006, followed by M.S. in Engineering and Applied Sciences from Harvard 2008 and an M.B.A. from...

https://nanohub.org/members/305162

7. 29 Jul 2020 | | Contributor(s):: Cindy Nguyen, Rei Sanchez-Arias

Root-finding methods and numerical optimization techniques with applications in science, engineering, and data analysis

8. 28 May 2020 | | Contributor(s):: Stanley H. Chan

9. 29 Apr 2020 | | Contributor(s):: Nathan Killoran

PennyLane is a Python-based software framework for optimization and machine learning of quantum and hybrid quantum-classical computations.

10. 28 Jan 2020 |

The purpose of the workshop is to bring different communities together, review recent theoretical and experimental advances and explore synergetic collaborations. The workshop aligns well with the significant investments in quantum technologies through the National Quantum Initiative in the...

11. 21 Jan 2020 | | Contributor(s):: Stanley H. Chan

12. 21 Jan 2020 | | Contributor(s):: Stanley H. Chan

13. 17 Jan 2020 | | Contributor(s):: Stanley H. Chan

Spring 2020 - This course is in productionCourse Website: https://engineering.purdue.edu/ChanGroup/ECE595/index.htmlCourse Outline:Part 1: Mathematical BackgroundLinear Regression and OptimizationPart 2: ClassificationMethods to train linear classifiersFeature analysis, Geometry, Bayesian...

14. 28 Oct 2019 | | Contributor(s):: Jacob Biamonte

We show that the variational approach to quantum enhanced algorithms admits a universal model of quantum computation.

15. 21 Aug 2019 | | Contributor(s):: Daniel Lidar

In this talk I will argue in favor of a different criterion I call "quantum algorithmic breakeven," which focuses on demonstrating an algorithmic scaling improvement in an error-corrected setting over the uncorrected setting. I will present evidence that current experiments with...

16. 14 Jun 2019 | | Contributor(s):: Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin

The individual Panel Talks which accompanies this discussion can be found here.Why do we need experiments?Are your methods “descriptive” or “predictive”?Do you work with any other theory/simulation groups?On the 5 year timescale: is machine-learning hype or a real...

17. 14 Jun 2019 | | Contributor(s):: Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin

The Panel Discussion which follows these individual presentations can be found here.Individucal Presentations:Theory and Machine Learning in the Chemical Sciences, Brett Matthew Savoie;Divide and Conquer with QM/MM Methods, Pradeep Kumar Gurunathan;Computational Chemistry/Materials, Peilin...

18. 30 May 2019 | | Contributor(s):: Saurabh Bagchi

19. 30 May 2019 | | Contributor(s):: Saurabh Bagchi

20. Peter Shor

Peter Shor is Morss Professor of Applied Mathematics since 2003, and Chair of the Applied Mathematics Committee since 2015. He received the B.A. in mathematics from Caltech in 1981, and the Ph.D....

https://nanohub.org/members/230531