ductho-le/WaveDL
A Scalable Deep Learning Framework for Wave-Based Inverse Problems
This framework helps scientists and engineers interpret complex wave data, such as from ultrasound or geological surveys, to reveal hidden physical properties or material characteristics. You feed in raw wave measurements like waveforms or B-scans, and it outputs insights like material composition, defect sizes, or damage locations. It's designed for researchers in fields like non-destructive evaluation, geophysics, or biomedical imaging who need to analyze large datasets efficiently.
Available on PyPI.
Use this if you are a researcher working with wave-based data and need a robust, scalable tool to train deep learning models that can accurately predict underlying physical properties or characteristics.
Not ideal if you are working with non-wave-based data or primarily need to classify images or text, as this tool is specifically optimized for inverse problems involving wave phenomena.
Stars
43
Forks
6
Language
Python
License
MIT
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
Dependencies
19
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