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| GNU Affero General Public License v3.0 | Apache License 2.0 |
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bamboo
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How is the Nusselt Number obtained for Regenerative cooling?
I worked with my university's rocketry society on making an open source application specifically for modelling regenerative cooling. It includes a paper explaining how we do everything, including calculating the Nusselt number (or specifically, the convective heat transfer coefficient). All the source code is here and the paper is here.
facet
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/r/technology top posts: Mar 1, 2021
FACET is an open source library for human-explainable AI. It combines sophisticated model inspection and model-based simulation to enable better explanations of your supervised machine learning models.\ (0 comments)
- FACET is an open source library for human-explainable AI. It combines sophisticated model inspection and model-based simulation to enable better explanations of your supervised machine learning models.
- Human-Explainable AI
- Facet: ML model inspection and model-based simulation for better explanations
What are some alternatives?
pydna - Clone with Python! Data structures for double stranded DNA & simulation of homologous recombination, Gibson assembly, cut & paste cloning.
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
transient_rotordynamic - transient dynamics of elastic rotors in journal bearings with Julia and Python
ethereum-economic-model - A modular dynamical-systems model of Ethereum's validator economics
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
open-space-toolkit - Collection of versatile software libraries for space engineering applications.
wordlescraper - Combine wordle statistics metrics from various locations, data science to correlate scores with words, and a front end to display the results.
transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).