WaveDL: A scalable deep learning framework for wave-based inverse problems
Description
Wave-based inverse problems, which determine material or structural parameters from measured wavefields, are fundamental to non-destructive evaluation (NDE), biomedical diagnostics, and geophysics. For repeated-query applications, deep learning shifts the computational effort from iterative inversion to offline training, enabling low-latency prediction, yet large-scale adoption remains limited by challenges in reproducibility, fair architecture comparison, large-dataset handling, and stable distributed training. This paper introduces WaveDL, an open-source Python framework that addresses these challenges through an integrated pipeline covering memory-efficient data streaming, a model zoo comprising 71 network variants adapted for multi-target regression, synchronized multi-GPU training, soft physics-constraint penalties, and automated deployment export via Open Neural Network Exchange (ONNX). Built on PyTorch and Hugging Face Accelerate, WaveDL enables a complete workflow from raw data to deployed model with a single command, lowering the barrier for domain scientists to apply deep learning without specialized machine learning expertise. The framework is demonstrated using a 120 GB Lamb wave dispersion curve dataset to evaluate out-of-core training and systematic cross-architecture benchmarking for finite-dimensional parameter inversion. WaveDL is freely installable via pip install wavedl.