lehmannfa
/

MIFNO

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 # MIFNO
 Train a Multiple-Input Fourier Neural Operator (MIFNO) to predict the solution of 3D source-dependent Partial Differential Equations (PDEs). The MIFNO is described in the article [Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamics](https://doi.org/10.1016/j.jcp.2025.113813). It extends the 3D Factorized Fourier Neural Operator (F-FNO) to PDEs with a source term. As such, the MIFNO contains a dedicated *source branch* that takes as input a vector of source parameters.
-![MIFNO](https://github.com/user-attachments/assets/e08b86b4-2374-41ee-8e4f-7a4ab8019805)
 ## Data
 The MIFNO is trained on the [HEMEW<sup>S</sup>-3D database](https://doi.org/10.57745/LAI6YU) that contains 30,000 simulations of the 3D elastic wave equation in heterogeneous media with different sources. The folder `HEMEWS3D_S32_Z32_T320_fmax5_rot0_test` contains 20 test samples that have already been preprocessed and are readily usable for machine learning tasks. To get the entire train dataset, you need to download the data from the [HEMEW^S-3D repository](https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/LAI6YU) and follow the [preprocessing pipeline](https://github.com/lehmannfa/HEMEW3D).

 # MIFNO
 Train a Multiple-Input Fourier Neural Operator (MIFNO) to predict the solution of 3D source-dependent Partial Differential Equations (PDEs). The MIFNO is described in the article [Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamics](https://doi.org/10.1016/j.jcp.2025.113813). It extends the 3D Factorized Fourier Neural Operator (F-FNO) to PDEs with a source term. As such, the MIFNO contains a dedicated *source branch* that takes as input a vector of source parameters.
+![MIFNO](https://github.com/lehmannfa/MIFNO/blob/main/assets/images/graphical_abstract.png)
 ## Data
 The MIFNO is trained on the [HEMEW<sup>S</sup>-3D database](https://doi.org/10.57745/LAI6YU) that contains 30,000 simulations of the 3D elastic wave equation in heterogeneous media with different sources. The folder `HEMEWS3D_S32_Z32_T320_fmax5_rot0_test` contains 20 test samples that have already been preprocessed and are readily usable for machine learning tasks. To get the entire train dataset, you need to download the data from the [HEMEW^S-3D repository](https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/LAI6YU) and follow the [preprocessing pipeline](https://github.com/lehmannfa/HEMEW3D).