search

SciML / ModelingToolkit.jl

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
https://mtk.sciml.ai/dev/
Other
1.39k stars 199 forks source link

Docs suggestions #2131

Open paulflang opened 1 year ago

paulflang commented 1 year ago
  1. Here, I think it should be rober! instead of rober.
  2. In the global structural identifiability tutorial: a. The docs say that "We can see that all states (except $x_7$) and all parameters are locally identifiable", despite the results don't show any states. b. Why does it say that "We can see that only parameters a, g are unidentifiable", when the result suggest only g is unidentifiable. c. In the case of inputs u1 and u2 It is unclear whether these inputs are known perturbations (to improve identifiability) or are unknown and shall be estimated (reduces identifiability). In case of the former, the example should test if g is now identifiable. In the case of the latter, it would still be interesting to know why we are not checking for all parameters. d. AFAIK structural identifiability methods scale poorly to large models. Maybe it would be good to add a sentence on scalability.
  3. The example for DAE Index Reduction uses structural_simplify(dae_index_lowering(traced_sys)), despite another place states that this is superfluous, as structural_simplify calls it internally. Maybe worth clarifying.
ChrisRackauckas commented 5 months ago

@AayushSabharwal let's make sure we take this into account with the overhaul