Merge pull request '[Upstream sync] ai-boost/awesome-ai-for-science (github) — 0 added, 1 modified' (#83) from upstream-sync/awesome-ai-for-science-20260821-ec6f36-ihmj into main

Reviewed-on: #83

upstream/ai-boost-awesome-ai-for-science/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/7f00e2f0/README.md
-upstream_sha: 7f00e2f0
-imported_at: 2026-08-18
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/ec6f3650/README.md
+upstream_sha: ec6f3650
+imported_at: 2026-08-21
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -303,6 +303,7 @@ validated: false
 - [Science-Star (USTC AI4Science, 2025)](https://github.com/ustc-ai4science/Science-Star) - Open-source platform for building, extending, and experimenting with scientific agents, providing modular agent construction tools and standardized evaluation pipelines for accelerating autonomous scientific discovery research (748+ stars, MIT License)
 - [SR-Scientist (ICLR 2026)](https://github.com/GAIR-NLP/SR-Scientist) - Scientific equation discovery with agentic AI, elevating LLMs from equation proposers to autonomous scientists that write code, analyze data, implement equations, and optimize based on experimental feedback; outperforms baselines by 6-35% across four science disciplines with robustness to noise and out-of-domain generalization (GAIR-NLP / SJTU, 49+ stars, Apache 2.0)
 - [ARA (Agent-Native Research Artifact)](https://github.com/ARA-Labs/Agent-Native-Research-Artifact) - Research ecosystem for rigorous and trustworthy AI scientists — a protocol and skill bundle that makes autonomous research verifiable, crystallized, and observable through structured, machine-executable research artifacts and five agent skills for research management, compilation, verification, visualization, and publication (ARA-Labs, 447+ stars, MIT License, 2026)
+- [XScientist](https://github.com/smileformylove/XScientist) - Local-first autonomous research system implementing a Git-like research protocol for long-running scientific discovery; explores competing explanations, executes experiments inside an isolation boundary, self-criticizes results, and exports the entire path as typed Agent-Native Research Artifacts (ARA) with exploration DAGs, claim-to-evidence anchors, content hashes, and re-execution hooks (126+ stars, Apache 2.0, arXiv 2026)
 - [Scholar Loop](https://github.com/renee-jia/scholar-loop) - Autonomous multi-agent AI scientist that mirrors a PhD workflow: literature review → grounded hypothesis → real ML experiments → self-critique → write-up; features a deterministic harness with frozen-metric scoring, edit allowlists, and a verified registry to make reward-hacking and hallucination impossible, plus 108 unit tests runnable without API keys or GPUs (461+ stars, MIT License, 2026)
 - [ResearchStudio (Microsoft)](https://github.com/microsoft/ResearchStudio) - AI co-author covering the entire research lifecycle — from an under-specified research direction to a published paper; includes ResearchStudio-Idea for evidence-grounded research ideation and ResearchStudio-Reel for turning finished papers into posters, narrated videos, blogs, and interactive reels; runs as skills on Claude Code and Codex (1.2K+ stars, MIT License, 2026)
 - [Principia](https://github.com/pzqpzq/Principia) - Principle-first scientific idea discovery framework that extracts reusable principles from public literature and private research materials, composes them into traceable Idea Cards with prior-art comparisons, and exports validation-ready research packs; emphasizes inspectable scientific objects, risk disclosure, and falsification paths (ICML 2026, 411+ stars, MIT License)
@@ -802,6 +803,7 @@ validated: false
 - [AgML](https://github.com/Project-AgML/AgML) - Agricultural machine learning platform
 - [FarmVibes.AI](https://github.com/microsoft/farmvibes-ai) - Multi-modal geospatial ML platform for agriculture and sustainability, fusing satellite imagery (RGB, SAR, multispectral), drone imagery, weather data, and sensor data for crop identification, carbon footprint estimation, and microclimate prediction (Microsoft Research, MIT License)
 - [PlantCV](https://github.com/danforthcenter/plantcv) - Open-source image analysis toolkit for high-throughput plant phenotyping, extracting morphological, color, and texture traits from RGB, hyperspectral, and thermal imagery with modular Python workflows for crop improvement, stress detection, and plant biology research (Donald Danforth Plant Science Center, 795+ stars, MPL-2.0)
+- [Virdis](https://github.com/Thanas-R/Virdis) - Satellite-powered agricultural and land analytics platform combining Sentinel-2 imagery, Google Earth Engine processing, real-time weather data, soil science databases, and AI-driven crop planning into a unified web dashboard (145+ stars, AGPL-3.0, 2026)
 
 #### Ecological Modeling
 - [BioSimulators](https://github.com/biosimulators/Biosimulators) - Biological simulation tools
@@ -823,6 +825,9 @@ validated: false
 
 ## 🏗 Engineering & Built Environment
 
+### Mechanical, Aerospace & Industrial Engineering
+- [Noether (Emmi AI)](https://github.com/Emmi-AI/noether) - Open software framework for Engineering AI built on transformer building blocks, enabling teams to build, train, and operate industrial simulation models across engineering verticals; includes ready-to-use recipes for CFD (AB-UPT on DrivAerML), external aerodynamics, and heat transfer (234+ stars, ENPL non-commercial license, 2026)
+
 ### Structural & Civil Engineering
 - [StructureClaw](https://github.com/structureclaw/structureclaw) - AI-assisted structural engineering workspace for AEC workflows: natural language to structural model, analysis, code-check, and report (171+ stars, MIT License, 2026)