A 2D attention operator is modified according to the integral operator formulation. The modified U-Net drop-in replacement is then used to solve an inverse problem (Electrical Impedance Tomography or EIT). The neural net is used to approximate the inclusion map using a single (or a few) current-to-voltage (Neumann-to-Dirichlet) data pairs. The boundary measurements are preprocessed using a PDE-based feature map.

Training model: --model args can be uit (integral transformer), ut (with traditional softmax normalization), hut (hybrid ut with linear attention), xut (cross-attention with hadamard product interaction), fno2d (Fourier neural operator 2d), unet (traditional UNet with CNN, big baseline, 33m params), unets (UNet with the same number of layers with U-integral transformer)

All different models' settings can be found in configs.yml. Default is to train a single input-channel

python run_train.py --model uit --parts 2 4 5 6

python evaluation.py --model uit # base integral transformer
python evaluation.py --model uit-c3 --channels 3 # 3 channels

@article{2022GuoCaoChenTransformer,
  title={Transformer Meets Boundary Value Inverse Problems},
  author={Guo, Ruchi and Cao, Shuhao and Chen, Long},
  journal={arXiv preprint arXiv:2209.14977},
  year={2022}
}

This work is supported in part by National Science Foundation grants DMS-1913080, DMS-2012465, and DMS-2136075. No additional revenues are related to this work.