diff --git a/_applications/2026-combustion.md b/_applications/2026-combustion.md
index 7c84ccfa710c..f0e9e11ed0c6 100644
--- a/_applications/2026-combustion.md
+++ b/_applications/2026-combustion.md
@@ -18,11 +18,14 @@ The applications focuse on the DLR methane/hydrogen/nitrogen turbulent diffusion
 
   * [DLR Flame CFD Workflow](#cfd-workflow)
   * [Tutorial](#cfd-tutorial)
+    
 * [AI & Data-Driven Models](#ai)
-
+  
   * [HOSVD + GPR Parametric Interpolation](#ai-hosvd-gpr)
   * [Tutorial](#hosvd-tutorial-and-post)
+    
 * [References](#references)
+  
 * [Contributors](#contributors)
 
 # CFD & High-Fidelity Simulations <a id="cfd"></a>
diff --git a/_research/ai-models/combustion/hosvd_gpr_combustion_post.md b/_research/ai-models/combustion/hosvd_gpr_combustion_post.md
index ee847388691d..ac6e787a0b16 100644
--- a/_research/ai-models/combustion/hosvd_gpr_combustion_post.md
+++ b/_research/ai-models/combustion/hosvd_gpr_combustion_post.md
@@ -1,11 +1,12 @@
 ---
-layout: page
+layout: post
+date: 2026-06-25
 title: "HOSVD + GPR: Parametric Surrogate for Turbulent Jet Flames"
 category: "AI & Data-Driven Models"
 topic: "Combustion"
 tldr: "Parametric interpolation of the DLR turbulent jet diffusion flame using Higher-Order SVD and Gaussian Process Regression to predict full combustion fields at unseen operating conditions."
 thumbnail: "assets/img/Tutorial/Combustion/hosvd_gpr/Re16000_mf012.png"
-permalink: /research/ai-combustion/
+permalink: /research/ai-combustion/hosvd-gpr-surrogate/
 ---
 
 <script>
@@ -116,10 +117,6 @@ Combining the interpolated coefficients with the core tensor and rescaling gives
 
 ![Reconstruction error per species for Re=13000, mf=0.08](/assets/img/Tutorial/Combustion/hosvd_gpr/relative_error_per_feat_Re13000_mf008.png)
 
-## Contributors
-
-Isacco Faglioni
-
 ## **References**
 
 [1] Aversano, et al., *Digital twin of a combustion furnace operating in flameless conditions: reduced-order model development from CFD simulations*, Proc. Combust. Inst. 38(4):5373–5381, 2021.
diff --git a/_tutorials/2026-combustion_tutorial.md b/_tutorials/2026-combustion_tutorial.md
index 3dad194ce838..90bba5b14719 100644
--- a/_tutorials/2026-combustion_tutorial.md
+++ b/_tutorials/2026-combustion_tutorial.md
@@ -30,7 +30,7 @@ The reference case is the DLR turbulent non-premixed jet flame. The burner consi
 
 <!-- IMAGES -->
 <p style="text-align: center;">
-    <img src="https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/DLR_burner_Geometry.png?raw=true" alt="DLR burner geometry" width="60%">
+    <img src="https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/DLR_burner_Geometry.png?raw=true" alt="DLR burner geometry" width="65%">
 </p>
 
 | Quantity | Description |
diff --git a/research/ai-combustion.md b/research/ai-combustion.md
index eb2e6d914f73..17c4f70affb4 100644
--- a/research/ai-combustion.md
+++ b/research/ai-combustion.md
@@ -7,7 +7,7 @@ subtitle: Data-driven and AI approaches to reactive flow modelling
 This section collects all the research done by our group regarding reduced-order modelling and AI-driven methods for combustion.
 
 <div class="row">
-  {% assign current_posts = site.research | where_exp: "post", "post.topic == 'Combustion' and post.category == 'AI & Data-Driven Models'" %}
+  {% assign current_posts = site.research | where: "topic", "Combustion" %}
   {% for post in current_posts %}
   <div class="col-md-12 mb-4">
     <div class="card flex-row" style="border: 1px solid #ddd; border-radius: 8px; overflow: hidden; padding: 15px;">