I think random matrices will produce misleading results. The chance of randomly generating a matrix that resembles an application is effectively zero. I think you'd be better off with some model problems varying parameters that control the physical regime (e.g., shifts to a Laplacian, advection with/without upwinding at varying cell Peclet numbers, varying nondimensional parameters, time step, and boundary layers for fluids, varying Poisson ratio and material contrast in elasticity).
"Sun, Yixuan via petsc-users" <petsc-users@mcs.anl.gov> writes: > Hello PETSc team, > > My name is Yixuan, and I am a postdoc at MCS. We are working on a project > where we want to predict the preconditioners from the corresponding matrices. > For preliminary exploration purposes, we generated random matrices and their > preconditioners using PETSc. The current deep learning models look promising > with the generated data. However, we are not sure about the correctness of > the data generation code and wanted to ask if you could help check the > correctness of our script (< 100 lines) and let us know if there were any > issues. Here is the > link<https://github.com/iamyixuan/MatrixPreNet/blob/main/src/utils/generate_training.py> > to the script. > > Please let me know if you have any questions or concerns. Thank you in > advance! > > > Warm regards, > Yixuan > -------------------- > Yixuan Sun > Postdoctoral Researcher > Mathematics and Computer Science > Argonne National Laboratory
