From 231fc302a696dc5d0ebb6d3b17afc2966a682bc7 Mon Sep 17 00:00:00 2001
From: ThummeTo <83663542+ThummeTo@users.noreply.github.com>
Date: Wed, 11 Sep 2024 11:02:37 +0200
Subject: [PATCH] readme update (#149)

---
 README.md     | 9 ++-------
 src/neural.jl | 2 +-
 2 files changed, 3 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 3dd541dd..25c31107 100644
--- a/README.md
+++ b/README.md
@@ -52,15 +52,10 @@ You can evaluate FMUs inside of your loss function.
 - Implicit solvers using `autodiff=true` is not supported (now), but you can use implicit solvers with `autodiff=false`.
 
 - Sensitivity information over state change by event $\partial x^{+} / \partial x^{-}$ can't be accessed in FMI. 
-These sensitivities are simplified on basis of one of the following assumptions (defined by user):
-(1) the state after event depends on nothing, so sensitivities are zero or 
-(2) the state after event instance only depends on the same state before the event instance
-The second is often correct for e.g. mechanical contacts, but may lead to wrong gradients for arbitrary discontinuous systems. 
-However even if the gradient might not be 100% correct in any case, gradients are often usable for optimization tasks. 
+These sensitivities are sampled if the FMU supports `fmiXGet/SetState`. If this feature is not available, wrong sensitivities are computed, which my influence your optimization (dependent on the use case).
 This issue is also part of the [*OpenScaling*](https://itea4.org/project/openscaling.html) research project.
 
-- Discontinuous systems with implicit solvers use continuous adjoints instead of automatic differentiation through the ODE solver.
-This might lead to issues, because FMUs are by design not capable of being simulated backwards in time. 
+- If continuous adjoints instead of automatic differentiation through the ODE solver (discrete adjoint) are applied, this might lead to issues, because FMUs are by design not capable of being simulated backwards in time. 
 On the other hand, many FMUs are capable of doing so.
 This issue is also part of the [*OpenScaling*](https://itea4.org/project/openscaling.html) research project.
 
diff --git a/src/neural.jl b/src/neural.jl
index 85cba25f..51df5cd0 100644
--- a/src/neural.jl
+++ b/src/neural.jl
@@ -152,7 +152,7 @@ mutable struct CS_NeuralFMU{F,C} <: NeuralFMU
     tspan::Any
 
     p::Union{AbstractArray{<:Real},Nothing}
-    re::Any # restrucure function
+    re::Any # restructure function
 
     snapshots::Bool