deep-learning-for-indentation | mechanical properties of materials through deep learning

by lululxvi Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | deep-learning-for-indentation Summary

deep-learning-for-indentation is a Python library. deep-learning-for-indentation has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Extraction of mechanical properties of materials through deep learning from instrumented indentation

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Security

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deep-learning-for-indentation has a low active ecosystem.

It has 19 star(s) with 10 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 2 have been closed. On average issues are closed in 183 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of deep-learning-for-indentation is current.

Quality

deep-learning-for-indentation has 0 bugs and 0 code smells.

Security

deep-learning-for-indentation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deep-learning-for-indentation code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

deep-learning-for-indentation is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

deep-learning-for-indentation releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

deep-learning-for-indentation saves you 315 person hours of effort in developing the same functionality from scratch.

It has 757 lines of code, 47 functions and 5 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed deep-learning-for-indentation and discovered the below as its top functions. This is intended to give you an instant insight into deep-learning-for-indentation implemented functionality, and help decide if they suit your requirements.

Test the inverse model
Calculates the probability density from Estar
Calculate Pi - 5 correlation coefficient
Inverse of the inverse model
Test the inverse of the inverse model
R Calculates the Pitheta model
R Generate the inverse of the inverse model
Compute the gradient of the GPE
Predict the mean and variance of the plot
Runs validation function
Compute FNN
Generate model forward
R Generate a forward model
Parses 4 angle matrix
Convert Estar to Estar
Generate the LMNN model
Train the model
Read 1 - angle from the FEM
Evaluate validation function
Reads the model
Read 2 - angle and 3 - angle data
Calculate the validation scaling of a dataset
Generate the inverse of the inverse model
Predict the mean and variance of a plot
Read the model
Fit a function to NTi
Compute SVR

Get all kandi verified functions for this library.

deep-learning-for-indentation Key Features

No Key Features are available at this moment for deep-learning-for-indentation.

deep-learning-for-indentation Examples and Code Snippets

No Code Snippets are available at this moment for deep-learning-for-indentation.

Community Discussions

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Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install deep-learning-for-indentation

You can download it from GitHub.
You can use deep-learning-for-indentation like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.