I haven't read the code, but I would expect simpl.cpp to have something to do with SiMPL https://github.com/dohyun-cse/mfem/blob/simpl2/miniapps/simp...
According to the paper (https://arxiv.org/abs/2411.19421) the code for SiMPL is implemented in MFEM and is available here: https://github.com/dohyun-cse/mfem/tree/simpl2.
Yes, there is hope for a high-level heuristic understanding. Here's my attempt to explain in more familiar terms. They train a new neural network from scratch for each problem. The network is trained only on the data…
> No one actually knows what their thresholds are (including library authors) If low-level numerical libraries provided documentation for their accuracy guarantees, it would make it easier to develop software on top of…
I've also been surprised many times by issues in numerical libraries. In addition to matrices with simple entries, I've found plenty of bugs just testing small matrices, with dimensions in {0,1,2,3,4}. Many…
I haven't read the code, but I would expect simpl.cpp to have something to do with SiMPL https://github.com/dohyun-cse/mfem/blob/simpl2/miniapps/simp...
According to the paper (https://arxiv.org/abs/2411.19421) the code for SiMPL is implemented in MFEM and is available here: https://github.com/dohyun-cse/mfem/tree/simpl2.
Yes, there is hope for a high-level heuristic understanding. Here's my attempt to explain in more familiar terms. They train a new neural network from scratch for each problem. The network is trained only on the data…
> No one actually knows what their thresholds are (including library authors) If low-level numerical libraries provided documentation for their accuracy guarantees, it would make it easier to develop software on top of…
I've also been surprised many times by issues in numerical libraries. In addition to matrices with simple entries, I've found plenty of bugs just testing small matrices, with dimensions in {0,1,2,3,4}. Many…