- Видео 10
- Просмотров 3 140
Rafid Bendimerad
Добавлен 19 мар 2018
Welcome to my channel !
Join me as I share my PhD research in Aerospace Engineering. This channel features conference presentations, miscellaneous projects, and tutorials designed to inspire and educate.
Join me as I share my PhD research in Aerospace Engineering. This channel features conference presentations, miscellaneous projects, and tutorials designed to inspire and educate.
Optimization of Refueling Strategies for Space Missions
Discover how strategic in-space refueling can revolutionize space missions by increasing payload capacity and mission efficiency. Using analytical models and the SMART-1 mission as a case study, we explore how refueling can optimize mission performance. Learn more about the trade-offs between payload, propellant consumption, and mission time in this innovative approach to space exploration.
#SpaceMissions #RefuelingOptimization #AerospaceEngineering #SciTech #AIAA
#SpaceMissions #RefuelingOptimization #AerospaceEngineering #SciTech #AIAA
Просмотров: 275
Видео
AI-Generated Podcast: Europa Clipper Mission
Просмотров 1833 месяца назад
Explore the Europa Clipper mission through an AI-generated discussion, delving into the mission's scientific goals and its significance in the exploration of Jupiter's moon, Europa. This podcast provides an insightful overview of the spacecraft's objectives, its trajectory, and the search for evidence of life in the moon's subsurface ocean. I have generated this podcast using Google NotebookLM ...
Foundations of Propulsion: Rocket Equation, Thrust, and Specific Impulse
Просмотров 1,2 тыс.5 месяцев назад
I gave this lecture for the "Propulsion of Spacecraft" course at Cornell University. This lecture includes the derivation of the most important equations used in propulsion; namely: the rocket equation, the thrust, and the specific impulse. #aerospaceengineering #rocketscience #spacecraft #cornelluniversity
Predicting the Suitability of Ionic Liquids for Electrospray Propulsion Using Machine Learning.
Просмотров 607 месяцев назад
This work was presented at the 38th edition of the International Electric Propulsion Conference (#IEPC2024). It explores the use of machine learning for propellant discovery. The goal is to predict if a given ionic liquid could serve as good propellant for electrospray thrusters that are used for micropropulsion of satellites in space missions.
Tutorial: N-body simulations of electrospray thruster plumes
Просмотров 2137 месяцев назад
In this tutorial, we'll guide you through simulating the expansion of a plume from an electrospray thruster. Learn to: - Set initial emission conditions - Describe external electric fields - Model molecule fragmentation - Compute forces on particles - Update particle positions - Visualize particle propagation in 2D and 3D The tutorial can be found on GitHub: github.com/AstraLab-Cornell/Tutorial...
Multiscale Modeling of Electrospray Thrusters Plume using Molecular Dynamics and N-body Simulations
Просмотров 223Год назад
This presentation offers an in-depth look at the application of multiscale modeling techniques to simulate the plume of electrospray thrusters, conducted as part of my A-exam requirement for the PhD program in Aerospace Engineering at Cornell University. Chapters: 00:00 Motivation 10:37 Full plume simulation (μm-mm scale) 21:04 Molecule-molecule interactions (Å-nm scale) 27:45 Molecule-surface ...
AI-Driven Discovery of Conductive Polymers for Lithium Batteries | Cornell University
Просмотров 114Год назад
As part of the course "AI for science" at Cornell University, we have presented our work about the generation of new-undiscovered-highly-conductive polymers for application in solid Lithium batteries. We proposed a Variational Autoencoder (VAE) approach that we benchmarked against a graph grammar approach by Guo et al (2022). #CornellUniversity #AIScience #MaterialScience #BatteryTechnology #VA...
Machine Learning in Astronomy: Classification of Stars, Galaxies, and Quasars using the SDSS Data.
Просмотров 569Год назад
In this video, we use supervised learning algorithms, namely Support Vector Machine (SVM) and Random Forests in order to classify stars, galaxies and quasars. Our data come from the Sloan Digital Sky Survey (SDSS). You can find the data, the code, and the final report of the project in this GitHub repo: github.com/Rafid-Ben/SSDS
Good job 👌
Thanks!
Great work 🤩
Thanks !
Great lecture
if you want to improve you can instead do 4 separate binary classifications using a random forest binary classifier and then use the predict proba function to get a confidence value and then remove everything under a threshold to get very very very very accurate predictions by trading off how many predictions you really do so it might do 1/100th the amount of predictions but they might be 99.9% accurate. Little trick form finance haha works even better when you are predicting across 100k things not like 3k stocks haha
Thanks for your comment. This seems to be an interesting classification strategy.
Punishing
Ok first of all, all of the information these AI are discussing sounds extremely convincing and real. I know a bit about Europa but can anyone confirm if some of what they said is false? It’s fascinating how these AI hint at the ability of knowing comedy and inserting it during a conversation here and there
What they are saying is correct because the discussion is based on a real research paper. I introduced a research paper written by humans, and the AI algorithm built this artificial conversation.
Both of them are machines? I am starting to think that the world portrayed in Black mirror is more human than ours
Yes, both of them are machines. I introduced a research paper written by humans, and the AI algorithm built this conversation.
How did you preprocess the data from SDSS cite? I want to get specific columns of the data. But I don't know how to access original database to get the columns same with your dataset in your github.
I obtained the data from Kaggle, here: www.kaggle.com/datasets/fedesoriano/stellar-classification-dataset-sdss17 And the code is on GitHub, here: github.com/Rafid-Ben/SSDS
Excellent.
🙏🏼
How did you access the data? Love this project, congratulations!
You can find the data, the code, and the final report of the project in this GitHub repo: github.com/Rafid-Ben/SSDS
Please send me the code or GitHub link
You can find the data, the code, and the final report of the project in this GitHub repo: github.com/Rafid-Ben/SSDS
hi there , i have been tasked to build the same tank for my university. Is there any published documents about this system?
Can you please do this in Matlab. Thank you
I have this Tutorial to reproduce the N-body simulations. But this is in Python, not Matlab. github.com/AstraLab-Cornell/Tutorial_Nbody.git
You travel with the stars
بالتوفيق
Amazing work proud of you 👏
Thank you for educating in this subject ✌️
have u published any research paper on this? If yes can u share it pls?
Hello, thank you for your interest. I haven't published any research paper on this. However, I have written a report about this work. There are some good references in the report that you can check. I can send it to you by email if you want.
You can find the data, the code, and the final report of the project in this GitHub repo: github.com/Rafid-Ben/SSDS