feat: refine queries and aesthetics

* issue #18 (#23) adjusted column width * Feat/table filter (#24) * issue #18 (fix column width) * issue #22 (add drop down menu) * Feature update queries for all categories (#31) * chore: add query terms to PINNS * chore: add query terms to PINNS * chore: change order of terms * chore: add query terms to neural ODEs * chore: further query terms * feat: add css file to main.html * feat: add css file to main.html * feat: edit font color * feat: remove the path to custom.css file * edit query for physics informed gnns * add time series forecasting tab * add Diffusion model section * add quotation marks to phrases * rearrange sequence of tabs * refine query for symbolic regression, include negative terms * change page length for all tables * update koopman query * adjust queries * add more categories to the overview page * feat: add manual articles for time-series forecasting algorithms (#32) * chore: add query terms to PINNS * chore: add query terms to PINNS * chore: change order of terms * chore: add query terms to neural ODEs * chore: further query terms * feat: add css file to main.html * feat: add css file to main.html * feat: edit font color * feat: remove the path to custom.css file * edit query for physics informed gnns * add time series forecasting tab * add Diffusion model section * add quotation marks to phrases * rearrange sequence of tabs * refine query for symbolic regression, include negative terms * change page length for all tables * update koopman query * adjust queries * add more categories to the overview page * add time-series forecasting manually curated papers --------- Co-authored-by: Gurdeep Singh <43615259+gurdeep330@users.noreply.github.com>
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diff --git a/app/data/manually_curated_articles.tsv b/app/data/manually_curated_articles.tsv
@@ -61,3 +61,19 @@ Symbolic regression	Discovery of Physics From Data: Universal Laws and Discrepan
 Symbolic regression	Data-driven discovery of partial differential equations (2017)	4575852	https://www.semanticscholar.org/paper/Data-driven-discovery-of-partial-differential-Rudy-Brunton/0acd117521ef5aafb09fed02ab415523b330b058?utm_source=direct_link
 Symbolic regression	Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control (2022)	244478236	https://www.semanticscholar.org/paper/Ensemble-SINDy%3A-Robust-sparse-model-discovery-in-Fasel-Kutz/883547fdbd88552328a6615ec620f96e39c57018?utm_source=direct_link
 Symbolic regression	Learning sparse nonlinear dynamics via mixed-integer optimization (2023)	249240669	https://www.semanticscholar.org/paper/Learning-sparse-nonlinear-dynamics-via-optimization-Bertsimas-Gurnee/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8?utm_source=direct_link
+Time-series forecasting	A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023)	259501265	https://www.semanticscholar.org/paper/A-Survey-on-Graph-Neural-Networks-for-Time-Series%3A-Jin-Koh/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9?utm_source=direct_link
+Time-series forecasting	Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency (2022)	 249848167	https://www.semanticscholar.org/paper/Self-Supervised-Contrastive-Pre-Training-For-Time-Zhang-Zhao/648d90b713997a771e2c49f02cd771e8b7b10b37?utm_source=direct_link
+Time-series forecasting	Domain Adaptation for Time Series Under Feature and Label Shifts (2023)	256627391	https://www.semanticscholar.org/paper/Domain-Adaptation-for-Time-Series-Under-Feature-and-He-Queen/5bd2c0acaf58c25f71617db2396188c74d29bf14?utm_source=direct_link
+Time-series forecasting	Graph-Guided Network for Irregularly Sampled Multivariate Time Series (2022)	238582772	https://www.semanticscholar.org/paper/Graph-Guided-Network-for-Irregularly-Sampled-Time-Zhang-Zeman/455bfc515eb279cc09023faa1f78c6efb61224ba?utm_source=direct_link
+Time-series forecasting	Taming Local Effects in Graph-based Spatiotemporal Forecasting (2023)	256662174	https://www.semanticscholar.org/paper/Taming-Local-Effects-in-Graph-based-Spatiotemporal-Cini-Marisca/e2a83369383aff37224170c1ae3d3870d5d9e419?utm_source=direct_link
+Time-series forecasting	AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs (2022)	258509669	https://www.semanticscholar.org/paper/AZ-whiteness-test%3A-a-test-for-signal-uncorrelation-Zambon-Alippi/c3c94ccc094dcf546e8e31c9a42506302e837524?utm_source=direct_link
+Time-series forecasting	Sparse Graph Learning from Spatiotemporal Time Series (2023)	249097457	https://www.semanticscholar.org/paper/Sparse-Graph-Learning-from-Spatiotemporal-Time-Cini-Zambon/0d01d21137a5af9f04e4b16a55a0f732cb8a540b?utm_source=direct_link
+Time-series forecasting	Graph Deep Learning for Time Series Forecasting (2023)	264439258	https://www.semanticscholar.org/paper/Graph-Deep-Learning-for-Time-Series-Forecasting-Cini-Marisca/ccea298edb788edf821aef58f0952c3e8debc25a?utm_source=direct_link
+Time-series forecasting	Graph state-space models (2023)	255415982	https://www.semanticscholar.org/paper/Graph-state-space-models-Zambon-Cini/279cd637b7e38bba1dd8915b5ce68cbcacecbe68?utm_source=direct_link
+Time-series forecasting	Large Language Models Are Zero-Shot Time Series Forecasters (2023)	263908782	https://www.semanticscholar.org/paper/Large-Language-Models-Are-Zero-Shot-Time-Series-Gruver-Finzi/123acfbccca0460171b6b06a4012dbb991cde55b?utm_source=direct_link
+Time-series forecasting	Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces (2024)	267364853	https://www.semanticscholar.org/paper/Graph-Mamba%3A-Towards-Long-Range-Graph-Sequence-with-Wang-Tsepa/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f?utm_source=direct_link
+Time-series forecasting	A decoder-only foundation model for time-series forecasting (2024)	264172792	https://www.semanticscholar.org/paper/A-decoder-only-foundation-model-for-time-series-Das-Kong/f45f85fa1beaa795c24c4ff86f1f2deece72252f?utm_source=direct_link
+Time-series forecasting	UniTS: Building a Unified Time Series Model (2024)	268201715	https://www.semanticscholar.org/paper/UniTS%3A-Building-a-Unified-Time-Series-Model-Gao-Koker/bcbcc2e1af8bcf6b07edf866be95116a8ed0bf91?utm_source=direct_link
+Time-series forecasting	Unified Training of Universal Time Series Forecasting Transformers (2024)	267411817	https://www.semanticscholar.org/paper/Unified-Training-of-Universal-Time-Series-Woo-Liu/4a111f7a3b56d0468f13104999844885157ef17d?utm_source=direct_link
+Time-series forecasting	Time-LLM: Time Series Forecasting by Reprogramming Large Language Models (2024)	263609325	https://www.semanticscholar.org/paper/Time-LLM%3A-Time-Series-Forecasting-by-Reprogramming-Jin-Wang/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277?utm_source=direct_link
+Time-series forecasting	Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series (2024)	266844130	https://www.semanticscholar.org/paper/Tiny-Time-Mixers-(TTMs)%3A-Fast-Pre-trained-Models-of-Ekambaram-Jati/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6?utm_source=direct_link
diff --git a/app/data/query.tsv b/app/data/query.tsv
@@ -1,7 +1,9 @@
 Title	Query
-Symbolic regression	((symbolic regression) + dynamics)
+Time-series forecasting	("time series forecasting")
+Diffusion models	(("diffusion model") | ("graph neural diffusion") | ("flow matching"))
+Latent Space Simulator	("latent space simulator") 
+Koopman Theory	(koopman*) | ("transformations in hilbert space") | ("linear transformation of PDEs") | ("regularization of physics-informed machine learning") -(mammalian) -(pneumonia)
+Symbolic regression	(("symbolic regression") | ("sparse identification of nonlinear dynamics") -("genetic programming") -("neural differential equations") -(evolution) -("gene expression")
 PINNs	("physics-informed neural computing") | ("physics-informed neural networks") | ("physics-informed deep learning")
-Latent Space Simulator	(VAMP) | (latent space simul*) | (decomposition of koopman operator) | (time-lagged autoencoder)
-Koopman Theory	(koopman*) | (transformations in hilbert space) | (linear transformation of PDEs) | (regularization of physics-informed machine learning)
-Neural ODEs	(neural ordinary differential equation) | (neural ODE) | (graph neural differential equation) | (graph neural diffusion) | (graph neural ODEs) | (neural ODE solvers)
-Physics-informed GNNs	((graph networks) + (physics constrain*)) | ("learned simulator"~1) | ("learned simulation"~1)
+Neural ODEs	("neural ordinary differential equation") | ("neural ODE") | ("graph neural differential equation")  | ("graph neural ODEs") | ("neural ODE solvers")
+Physics-informed GNNs	(("graph networks") + ("physics constrain*"") | ("Differentiable Learned Simulator*")
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -4,10 +4,12 @@ nav:
 - Overview: index.md
 - Physics-informed GNNs: Physics-informed_GNNs.md
 - Neural ODEs: Neural_ODEs.md
-- Koopman Theory: Koopman_Theory.md
-- Latent Space Simulator: Latent_Space_Simulator.md
 - PINNs: PINNs.md
 - Symbolic regression: Symbolic_regression.md
+- Koopman Theory: Koopman_Theory.md
+- Latent Space Simulator: Latent_Space_Simulator.md
+- Diffusion models: Diffusion_models.md
+- Time-series forecasting: Time-series_forecasting.md
 plugins:
 - mkdocstrings:
     paths:

diff --git a/overrides/main.html b/overrides/main.html
@@ -42,7 +42,7 @@
         {"className": "dt-center", "targets": "_all"},
         { width: '30%', targets: 0 }
       ],
-    pageLength: 5,
+    pageLength: 10,
     layout: {
         topStart: {
             buttons: ['copy', 'csv', 'excel', 'pdf', 'print']
@@ -55,7 +55,7 @@
         {"className": "dt-center", "targets": "_all"},
         { width: '30%', targets: 0 }
       ],
-    pageLength: 5,
+    pageLength: 10,
     layout: {
         topStart: {
             buttons: ['copy', 'csv', 'excel', 'pdf', 'print']
@@ -99,7 +99,7 @@
         {"className": "dt-center", "targets": "_all"},
         { width: '25%', targets: 1 }
       ],
-    pageLength: 5,
+    pageLength: 10,
     layout: {
         topStart: {
             buttons: ['copy', 'csv', 'excel', 'pdf', 'print']
@@ -143,7 +143,7 @@
         {"className": "dt-center", "targets": "_all"},
         { width: '30%', targets: 0 }
       ],
-    pageLength: 5,
+    pageLength: 10,
     layout: {
         topStart: {
             buttons: ['copy', 'csv', 'excel', 'pdf', 'print']

diff --git a/templates/overview.txt b/templates/overview.txt
@@ -29,7 +29,7 @@
     before delving into specific details within individual tabs.
     Please refer to the tabs 
     <b> Physics-informed GNNs, Neural ODEs, Koopman Theory, 
-    Latent Space Simulator, PINNs and Symbolic regression </b>
+    Latent Space Simulator, PINNs, Symbolic regression, Diffusion models and Time-series forecasting </b>
     for detailed information.
   </p>