{"id":17143823,"url":"https://github.com/asem000/wiggly-net","last_synced_at":"2025-03-24T09:42:37.284Z","repository":{"id":140101868,"uuid":"295945576","full_name":"ASEM000/Wiggly-NET","owner":"ASEM000","description":"CNN-LSTM based network to predict the oscillatory motion of silicon jet impinging sharp density interface","archived":false,"fork":false,"pushed_at":"2020-11-23T02:47:29.000Z","size":1424,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-29T15:13:17.932Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ASEM000.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-09-16T06:39:20.000Z","updated_at":"2021-01-15T11:09:21.000Z","dependencies_parsed_at":null,"dependency_job_id":"20a615ee-1fad-4f9b-8038-37b767811dff","html_url":"https://github.com/ASEM000/Wiggly-NET","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ASEM000%2FWiggly-NET","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ASEM000%2FWiggly-NET/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ASEM000%2FWiggly-NET/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ASEM000%2FWiggly-NET/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ASEM000","download_url":"https://codeload.github.com/ASEM000/Wiggly-NET/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245248005,"owners_count":20584459,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-14T20:42:23.233Z","updated_at":"2025-03-24T09:42:37.264Z","avatar_url":"https://github.com/ASEM000.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![DOI](https://zenodo.org/badge/295945576.svg)](https://zenodo.org/badge/latestdoi/295945576)\n\n# Wiggly-NET\nCNN-LSTM based network to predict the oscillatory motion of silicone jet impinging sharp density interface\nPart of the WINTER/SPRING 2020 undergraduate research program (URP) @ KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY (KAIST)\nThe prediction is generated by recursive input into the model\n\n### loss plot\n![Image](https://i.imgur.com/Z983VXD.png)\n\n### Example 1\n**Ground truth (top) , recurisvely predicted (bottom)**\n![Image](https://i.imgur.com/ZSwdD3b.png)\n\n### Example 2\n**Ground truth (top) , recurisvely predicted (bottom)**\n![Image](https://i.imgur.com/ii3xuhD.png)\n\n### Example 3\n**Ground truth (top) , recurisvely predicted (bottom)**\n![Image](https://i.imgur.com/ZkMs6UB.png)\n\n\n\n### this is a substep in the analysis of this phenonmenon\n![Image](https://i.imgur.com/wWrdWEj.png)\n\n### simulation of this phenomenon using Ansys fluent [youtube link](https://www.youtube.com/watch?v=hXc3pOpEeXc\u0026feature=youtu.be)\n\n\n## How to use\n\n### 0. Convert your video data into numpy array\nconvert your video to numpy array in the shape of 4D array ( number of frames , row size , col size , channel )\n\n### 1. Load the converted array using Wiggly UI notebook\n\n### 2.Choose the ROI you want predict its frames\nLarger dimensions require longer training time as well as larger model\n\n![Image](https://i.imgur.com/iQRbc2i.png)\n\n\n### 3.Convert video to training data \nDefine \n\n1. l=2,    **sequence length**\n2. df=6,   **frame step**\n3. test_size= 0.05,   **Test size 5%**\n4. verbose=True    **Print useful info**\n \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fasem000%2Fwiggly-net","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fasem000%2Fwiggly-net","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fasem000%2Fwiggly-net/lists"}