Restore ProblemTypeCtx as the discretization metadata key (#99)

* Revert PDEBaseMetadataCtx back to ProblemTypeCtx

Substituting a PDEBase-owned metadata key for `ProblemTypeCtx` silently
broke the MethodOfLines solution interface. `ProblemTypeCtx` is not a
private helper that can be reproduced locally: ModelingToolkitBase reads
it to populate `prob.problem_type`, and SciMLBase's `wrap_sol` dispatches
on that value to build a `PDETimeSeriesSolution`. With a different key the
metadata never reaches `problem_type`, `wrap_sol` falls through to the
identity method, and a raw `ODESolution` comes back with no error at
discretize or solve time, so `sol[u(t,x)]` fails.

Import it from ModelingToolkitBase, the owner, rather than through the
ModelingToolkit re-export. That satisfies the owner check; the remaining
publicness check needs the upstream `public` declaration in
SciML/ModelingToolkit.jl#4842, so keep one narrow
QA ignore until that ships.

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0117aJvytDT3y2jMi5Ng6Q5m

* Require ModelingToolkitBase 1.58 and drop the last QA ignore

`ProblemTypeCtx` is public as of ModelingToolkitBase 1.58.0
(SciML/ModelingToolkit.jl#4842), so PDEBase can
import it from its owner with no exception. QA is now fully strict.

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0117aJvytDT3y2jMi5Ng6Q5m

---------

Co-authored-by: ChrisRackauckas-Claude <accounts@chrisrackauckas.com>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>

Author: 3 people

Project.toml

@@ -20,7 +20,7 @@ AllocCheck = "0.2"
 BenchmarkTools = "1"
 DomainSets = "0.7, 0.8"
 IntervalSets = "0.7"
-ModelingToolkitBase = "1.57"
+ModelingToolkitBase = "1.58"
 ModelingToolkit = "11"
 PrecompileTools = "1.2.1"
 SciMLBase = "2.149.0, 3.1"

src/PDEBase.jl

@@ -6,8 +6,8 @@ import PrecompileTools
 import SciMLBase
 import Symbolics
 import SymbolicUtils
-using ModelingToolkitBase: PDESystem, System, get_bcs, get_connector_type, get_domain,
-    get_dvs, get_eqs, get_gui_metadata, get_iv, get_ivs, get_metadata, get_ps,
+using ModelingToolkitBase: PDESystem, ProblemTypeCtx, System, get_bcs, get_connector_type,
+    get_domain, get_dvs, get_eqs, get_gui_metadata, get_iv, get_ivs, get_metadata, get_ps,
     get_systems, get_unknowns, mtkcompile
 using PrecompileTools: @compile_workload, @setup_workload
 using SciMLBase: AbstractDiscretization, AbstractDiscretizationMetadata, NonlinearProblem,
@@ -74,8 +74,6 @@ conditions are discretized.
 """
 abstract type AbstractDiscretizationState end
 
-struct PDEBaseMetadataCtx end
-
 include("symbolic_utils.jl")
 include("utils.jl")
 include("variable_map.jl")

src/discretization_state.jl

@@ -126,7 +126,7 @@ function generate_system(
             eqs = map(_normalize_nonlinear_eq, alleqs)
             sys = System(
                 eqs, alldepvarsdisc, ps, initial_conditions = sys_defaults, name = name,
-                metadata = [PDEBaseMetadataCtx => metadata], checks = checks
+                metadata = [ProblemTypeCtx => metadata], checks = checks
             )
             return sys, nothing
         else
@@ -138,7 +138,7 @@ function generate_system(
                 initialization_eqs = init_eqs,
                 guesses = guesses,
                 name = name,
-                metadata = [PDEBaseMetadataCtx => metadata], checks = checks
+                metadata = [ProblemTypeCtx => metadata], checks = checks
             )
             return sys, tspan
         end
@@ -164,7 +164,7 @@ function SciMLBase.discretize(
     return try
         simpsys = mtkcompile(sys)
         if tspan === nothing
-            add_metadata!(getmetadata(sys, PDEBaseMetadataCtx, nothing), sys)
+            add_metadata!(getmetadata(sys, ProblemTypeCtx, nothing), sys)
             # MTK v11 requires symbolic map for initial guess
             unknowns_list = get_unknowns(simpsys)
             u0_guess = Dict(u => 1.0 for u in unknowns_list)
@@ -173,7 +173,7 @@ function SciMLBase.discretize(
                 discretization.kwargs..., kwargs...
             )
         else
-            mol_metadata = getmetadata(simpsys, PDEBaseMetadataCtx, nothing)
+            mol_metadata = getmetadata(simpsys, ProblemTypeCtx, nothing)
             add_metadata!(mol_metadata, sys)
             prob = ODEProblem(
                 simpsys, nothing, tspan;