A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on Physics-Informed Machine Learning (PIML) and Physics-Informed Neural Networks (PINNs).
In this repository, we would see different available libraries for Exploratory Data Analysis
A brutally interactive developer portfolio and project showcase. Features a 3D knowledge graph, dynamic visual themes, and a suite of built-in mini-apps, serving as a digital garden for my code and creativity.
Main codes for half-cell model, PINN and co-kriging implemented for physics-informed degradation diagnostics project: https://doi.org/10.1016/j.ensm.2024.103343
PIML (Parenthesis Intended Markup Language) encoder/decoder for Javascript
Parenthesis Intended Markup Language
PIML library for go
a tailored physics-informed neural network for SPDE
A lightweight, terminal-based YouTube audio player written in Go with synced lyrics and advanced playback controls.
A Hybrid Physics-Informed Machine Learning (PIML) System for Iraqi Concrete Optimization. Integrating DoE, Transfer Learning, and Lean Six Sigma for Engineering Certainty. [Phase 1: EDA]
MLOps-driven machine learning engine with deep user and social network insights for personalized, real-time product suggestions. A Physics Informed Machine Learning Project.
📄 Parse and stringify data easily with piml.js, a simple JavaScript library for PIML format, designed for human-readable configurations and data structures.
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