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Sign up for group presentations #2

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jedbrown opened this issue Dec 6, 2023 · 11 comments
Open

Sign up for group presentations #2

jedbrown opened this issue Dec 6, 2023 · 11 comments

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@jedbrown
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jedbrown commented Dec 6, 2023

Please comment here with the day you wish to present, tag your group members, and state the name of your project and a sentence or two about your objective. Please link to your repository and/or presentation slides when ready (but sign up now).

1️⃣ Monday, Dec 11
2️⃣ Wednesday, Dec 13

@virditus
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virditus commented Dec 6, 2023

Preferred presentation day: 2️⃣ Wednesday, Dec 13

Project name: Firedrake heat equation demos

Objective: To provide a couple of insightful demos that could potentially work in conjunction with existing documentation

Presentation link (Google Slides)

@MaYeatCo
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MaYeatCo commented Dec 6, 2023

Preferred day: Monday, Dec 11

Together with @Christina-wg

Title: Modeling 2D Vortex using PyClaw

Description: We created a jupyter notebook that guides the user through modeling of vortex evolution. We use standard Clawpack and SharpClaw packages for Riemann solvers. We note the difference between those two methods when applied to the vortex problem.

Details (theory, code, references etc.) can be found in the issue and the Jupyter notebook.

@mayanpoll
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mayanpoll commented Dec 7, 2023

Preferred presentation day: 2️⃣ Wednesday, Dec 13

Together with Amit Dubey and Galen Graham-Bell.

Title: Experiments in Vortex Shedding with SU2.

Description: We compare different solvers, limiters and meshes for vortex shedding for a cylinder. We also compare our results against published literature and we will share our findings through a post on a SU2 subforum.

Presentation:
Numerical-PDEs-Project.pdf

@SumedhSCU
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SumedhSCU commented Dec 8, 2023

Preferred Presentation day:
1️⃣ Monday, Dec 11
Library Chosen: PythonFOAM
Topic: PINNs for Poisson Equation and a comparison of OpenFOAM, NumPy and PETSc solvers

@tao-geo
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tao-geo commented Dec 8, 2023

Preferred presentation day:

Friday, Dec 8

Project is about comparing accuracy and efficiency of different solvers (SUPG, Entropy Viscosity stabilization, Geometric multigrid) in ASPECT.

Tao_Yuan_ASPECT_Project.pptx

@shrihariravi8004
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shrihariravi8004 commented Dec 8, 2023

Preferred presentation date: Wednesday, 13th December.

Presenting with: @Luro8836
Google slides: slides
Project: comparing a least squares based meshless discretisation method to OpenFOAM and its various solution schemes, for the inviscid Shock Tube problem.

@Luro8836
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Preferred presentation: Wednesday, 13th December.

Project: comparing a HLLC method to OpenFOAM and its various solution schemes, for the inviscid Shock Tube problem.

@Xiongwei-Liu
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Preferred presentation: Monday, Dec 11

Project: Compare 1D, 2D and 3D waveform simulation. Their advantages and disadvantages, how to choose and combine different ways in real applications.

SEM_Presentation_2.pptx

@Adam-Prieto
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Preferred day: Monday, Dec 11

Alone

Title: Analyzing the Time and Memory Complexity of Two Numerical PDE Packages in Python

Description:
After much deliberation and thought, I decided to analyze the time and memory complexity of Clawpack and FiPy when solving two types of numerical PDE problems.

@shipradsingh
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shipradsingh commented Dec 11, 2023

Preferred Day: 2️⃣ Wednesday, Dec 13

Alone

Title : Benchmarking MueLu and ML Libraries of Trilinos on the problem of Elasticity

Description : After Jed's feedback, I decided to take a little detour to analyze the performance metrics of Solve Time and Iteration Count on the problem of Elasticity for MueLu and ML libraries of Trilinos using a python script making use of PyTrilinos.

Presentation Link
Benchmarking Script

@YichenDong
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YichenDong commented Dec 12, 2023

Preferred Day: 2️⃣ Wednesday, Dec 13

Alone

Title :Performance Analysis of Different Solvers/Preconditioners in PETSc/Ratel

https://github.com/cu-numpde/fall23-community-YichenDong.git

Description : This project presents a performance analysis of different solvers and preconditioners in PETSc/Ratel. The objective is to understand the efficiency and effectiveness of these methods in solving a specific problem.

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