{"id":24374589,"url":"https://github.com/nomcodio-automation-systems/climalibtorch","last_synced_at":"2025-08-18T11:46:21.879Z","repository":{"id":272249297,"uuid":"915962374","full_name":"Nomcodio-Automation-Systems/ClimaLibTorch","owner":"Nomcodio-Automation-Systems","description":"ClimaLibTorch is a machine learning project leveraging neural networks to explore climate data predictions. The project includes a PyTorch-based model, utility tools for metrics like R2 and divergence, and a pipeline for data handling, training, and evaluation. ","archived":false,"fork":false,"pushed_at":"2025-01-13T07:42:05.000Z","size":21,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-03-12T11:48:49.308Z","etag":null,"topics":["climate-data","cpp","data-processing","libtorch","neural-network"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Nomcodio-Automation-Systems.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":"2025-01-13T07:33:25.000Z","updated_at":"2025-01-13T07:58:49.000Z","dependencies_parsed_at":null,"dependency_job_id":"ab3f17af-31ae-4488-8773-3e409e268e73","html_url":"https://github.com/Nomcodio-Automation-Systems/ClimaLibTorch","commit_stats":null,"previous_names":["nomcodio-automation-systems/climalibtorch"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Nomcodio-Automation-Systems/ClimaLibTorch","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nomcodio-Automation-Systems%2FClimaLibTorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nomcodio-Automation-Systems%2FClimaLibTorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nomcodio-Automation-Systems%2FClimaLibTorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nomcodio-Automation-Systems%2FClimaLibTorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Nomcodio-Automation-Systems","download_url":"https://codeload.github.com/Nomcodio-Automation-Systems/ClimaLibTorch/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nomcodio-Automation-Systems%2FClimaLibTorch/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270988055,"owners_count":24680662,"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","status":"online","status_checked_at":"2025-08-18T02:00:08.743Z","response_time":89,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["climate-data","cpp","data-processing","libtorch","neural-network"],"created_at":"2025-01-19T05:40:49.992Z","updated_at":"2025-08-18T11:46:21.853Z","avatar_url":"https://github.com/Nomcodio-Automation-Systems.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ClimaLibTorch Experiment\n\nThis project explores using a simple neural network for climate data predictions.\n\n---\n\n## Scientific Overview: ClimaLibTorch Experiment\n\nThis project investigates the application of machine learning, specifically a neural network-based approach, to model and predict climate-related data patterns. The study leverages foundational scientific principles of data-driven modeling and computational neural networks, aiming to identify patterns in potentially complex and nonlinear climate datasets.\n\n---\n\n## Core Components\n\n1. **Neural Network for Climate Modeling**\n    - The neural network, implemented in PyTorch, approximates functions mapping input climate features (e.g., temperature, precipitation) to target outputs (e.g., future conditions or classifications).\n    - **Scientific Basis**: Neural networks are particularly well-suited for capturing nonlinear relationships, which are pervasive in climate systems due to feedback loops and external forcings.\n\n2. **Metrics for Model Evaluation**\n    - Metrics such as the R2 score and divergence assess how well the model captures the variability and distribution of the data.\n    - **Scientific Basis**: These metrics evaluate the model's predictive power, ensuring that predictions are statistically grounded rather than arbitrary.\n\n3. **Data and Training**\n    - The data pipeline includes CSV parsing and preprocessing, which are critical for minimizing noise and ensuring meaningful input-output mappings.\n    - **Scientific Basis**: High-quality, representative data is essential for generalizable models. Training loops optimize the model parameters through iterative backpropagation.\n\n---\n\n## Experiment Context\n\n### Goals\n- Test the feasibility of using simple neural network architectures for climate data modeling.\n- Explore potential predictive capabilities and insights into climate dynamics.\n\n### Challenges\n- Climate data is often sparse, inconsistent, or highly variable, making it difficult to achieve robust training.\n- Limited data availability in 2022 hindered model generalization.\n\n### Outcomes\n- The experiment highlighted significant gaps in the dataset, underscoring the need for richer, more consistent climate data for meaningful machine learning applications.\n\n---\n\n## Broader Implications\n\nThis experiment represents an initial step toward leveraging neural networks for climate science. It underscores the importance of:\n\n- **Data availability**: Machine learning methods require abundant, high-quality data for accurate modeling.\n- **Complex model architectures**: Future studies might explore advanced models (e.g., transformers, ensemble methods) to handle the intricacies of climate data.\n\nWhile this experiment faced limitations, it lays a foundation for further exploration of machine learning in understanding and predicting climate dynamics, a critical area in addressing climate change challenges.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnomcodio-automation-systems%2Fclimalibtorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnomcodio-automation-systems%2Fclimalibtorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnomcodio-automation-systems%2Fclimalibtorch/lists"}