modelflows · modelflows · Jul 2, 2026 · Jun 24, 2026 · Jun 24, 2026 · Jun 24, 2026
diff --git a/_applications/2026-cardiac-pathology.md b/_applications/2026-cardiac-pathology.md
@@ -50,7 +50,7 @@ This section presents the tutorials to use the methods combining Artificial Inte
 
 The complete step-by-step tutorials are available in the [*Diagnosis tutorial*](https://modelflows.github.io/modelflowsapp/software/tutorials/cardiac-tutorials/#diagnosis-tutorial) and [*Prognosis tutorial*](https://modelflows.github.io/modelflowsapp/software/tutorials/cardiac-tutorials/#prognosis-tutorial) sections of the [*Cardiac tutorials*](https://modelflows.github.io/modelflowsapp/software/tutorials/cardiac-tutorials/) page.
 
-### Notebooks Diagnosis and Prognosis <a id="ai-notebooks"></a>
+### Notebooks <a id="ai-notebooks"></a>
 
 - [*Diagnosis Notebooks*](https://github.com/modelflows/ModelFLOWs-cardiac/tree/main/diagnosis_tutorial/notebooks): this link goes to the source files and to the guide of how to use the codes implementing the training and testing of models for the diagnosis of cardiovascular diseases (CVD).
 - [*Prognosis Notebooks*](https://github.com/modelflows/ModelFLOWs-cardiac/tree/main/prognosis_tutorial/notebooks): this link goes to the source files and to the guide of how to use the codes implementing the training and testing of models for the prognosis of heart failures.
@@ -65,7 +65,7 @@ The complete tutorial is available in the [*Interface tutorial*](https://modelfl
 
 The tutorial of the developed framework based on deep learning for automated left-ventricle (LV) segmentation and ejection-fraction (EF) prediction from echocardiograms is available in the [*LV Segmentation and EF estimation tutorial*](https://modelflows.github.io/modelflowsapp/software/tutorials/cardiac-tutorials/#lv-seg-ef-tutorial) section of the [*Cardiac tutorials*](https://modelflows.github.io/modelflowsapp/software/tutorials/cardiac-tutorials/).
 
-### Notebooks Segmentation and EF Estimation Tutorial <a id="ai-notebooks-seg-ef"></a>
+### Notebooks <a id="ai-notebooks_ef"></a>
 
 - [*LV segmentation and EF prediction*](https://github.com/modelflows/ModelFLOWs-cardiac/tree/main/segmentation%26EFprediction_tutorial/EchoNet-dynamic/scripts): this link goes to the source files necessary to perform the left-ventricle (LV) segmentation and the ejection fraction (EF) estimation from echocardiography images.
 

diff --git a/_applications/2026-combustion.md b/_applications/2026-combustion.md
@@ -19,11 +19,14 @@ 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/air-pollution/2026_air_pollution.md b/_research/ai-models/air-pollution/2026_air_pollution.md
@@ -75,12 +75,6 @@ This work demonstrates that Robust Data Envelopment Analysis provides a reliable
 By combining DEA and Robust Optimization, the proposed methodology reduces sensitivity to data fluctuations and offers a more realistic assessment of urban sustainability performance.
 The framework can support policy evaluation, urban planning, energy management, and sustainable development strategies in complex socio-economic systems.
 
-## Scientific Contribution
-
-This work demonstrates that Robust Data Envelopment Analysis provides a reliable framework for evaluating urban energy efficiency under uncertainty.
-By combining DEA and Robust Optimization, the proposed methodology reduces sensitivity to data fluctuations and offers a more realistic assessment of urban sustainability performance.
-The framework can support policy evaluation, urban planning, energy management, and sustainable development strategies in complex socio-economic systems.
-
 ## Relevance for ModelFLOWs
 
 This research illustrates the application of advanced data-driven methodologies to large-scale urban systems.

diff --git 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
@@ -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
@@ -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;">