Point to the renamed GitHub repo; keep the project title

NOTICE

@@ -1,4 +1,4 @@
-CastSolid GNN (casting solidification surrogate)
+Casting Solidification GNN
 Copyright 2026 Eugen Miknevic
 
 This product includes software and trained model weights developed by

README.md

@@ -24,9 +24,9 @@ tags:
   - scikit-learn
 ---
 
-# CastSolid GNN v5.1: casting solidification surrogate
+# Casting solidification GNN (v5.1)
 
-CastSolid GNN is a graph neural network surrogate for screening how metal castings solidify. You give it
+This is a graph neural network surrogate for screening how metal castings solidify. You give it
 a STEP model, an alloy and a mold condition. The pipeline meshes the part, predicts the
 solidification time at every mesh node and marks the regions that freeze last. Those hot spots
 are where feeding problems and shrinkage defects usually start.
@@ -55,7 +55,7 @@ feeding difficulty and shrinkage risk. It is not a porosity label.
 | Training data | FEniCSx enthalpy-method conduction simulations: about 9.7k primitive cases, 4.7k multi-alloy and multi-BC cases, 500 real-part cases |
 | Framework | PyTorch 2.3, PyTorch Geometric 2.7, scikit-learn 1.5.2, NumPy 2 |
 | License | Apache-2.0 for code and weights |
-| Code | Included in this repository. Showcase on [GitHub](https://github.com/eugenmik/casting-gnn-solidification) |
+| Code | Included in this repository. Showcase on [GitHub](https://github.com/eugenmik/castsolid-gnn) |
 | Author | Eugen Miknevic, independent foundry engineer |
 
 ## Contents
@@ -449,9 +449,9 @@ Code and weights are released under the [Apache License 2.0](LICENSE). The table
 against your actual alloy and process.
 
 ```bibtex
-@software{miknevic_castsolid_gnn_2026,
+@software{miknevic_casting_gnn_2026,
   author  = {Miknevic, Eugen},
-  title   = {CastSolid GNN: a graph neural network surrogate for
+  title   = {Casting Solidification GNN: a graph neural network surrogate for
              rapid solidification screening of metal castings},
   version = {5.1},
   year    = {2026},