Update index.md

Author: ChrisRackauckas

docs/src/index.md

@@ -1,29 +1,17 @@
-# NonlinearSolve.jl: High-Performance Unified Nonlinear Solvers
+# Integrals.jl: Unified Integral Approximation Interface
 
-NonlinearSolve.jl is a unified interface for the nonlinear solving packages of
-Julia. It includes its own high-performance nonlinear solvers which include the
-ability to swap out to fast direct and iterative linear solvers, along with the
-ability to use sparse automatic differentiation for Jacobian construction and
-Jacobian-vector products. It interfaces with other packages of the Julia ecosystem
+Integrals.jl is a unified interface for the numerical approximation of integrals
+(quadrature) in Julia. It interfaces with other packages of the Julia ecosystem
 to make it easy to test alternative solver packages and pass small types to
-control algorithm swapping. It also interfaces with the
-[ModelingToolkit.jl](https://mtk.sciml.ai/dev/) world of symbolic modeling to
-allow for automatically generating high-performance code.
-
-Performance is key: the current methods are made to be highly performant on
-scalar and statically sized small problems, with options for large-scale systems.
-If you run into any performance issues, please file an issue. Note that this
-package is distinct from [SciMLNLSolve.jl](https://github.com/SciML/SciMLNLSolve.jl).
-Consult the [NonlinearSystemSolvers](@ref nonlinearsystemsolvers) page for
-information on how to import solvers from different packages.
+control algorithm swapping.
 
 ## Installation
 
-To install NonlinearSolve.jl, use the Julia package manager:
+To install Integrals.jl, use the Julia package manager:
 
 ```julia
 using Pkg
-Pkg.add("NonlinearSolve")
+Pkg.add("Integrals")
 ```
 
 ## Contributing
@@ -34,12 +22,5 @@ Pkg.add("NonlinearSolve")
 - There are a few community forums:
     - the #diffeq-bridged channel in the [Julia Slack](https://julialang.org/slack/)
     - [JuliaDiffEq](https://gitter.im/JuliaDiffEq/Lobby) on Gitter
-    - on the Julia Discourse forums (look for the [modelingtoolkit tag](https://discourse.julialang.org/tag/modelingtoolkit)
+    - on the [Julia Discourse forums](https://discourse.julialang.org/)
     - see also [SciML Community page](https://sciml.ai/community/)
-
-## Roadmap
-
-The current algorithms should support automatic differentiation, though improved
-adjoint overloads are planned to be added in the current update (which will make
-use of the `f(u,p)` form). Future updates will include standard methods for
-larger scale nonlinear solving like Newton-Krylov methods.