WaveDL: A Scalable Deep Learning Framework for Wave-Based Inverse Problems
Abstract
WaveDL is a deep learning framework built for wave-based inverse problems — from ultrasonic NDE and geophysics to biomedical tissue characterization. It provides a robust, scalable training pipeline for mapping multi-dimensional data to physical quantities, with support for memory-mapped datasets, multi-GPU distributed training, and HPC deployment.
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 6 January 2026