update TOC

_toc.yml

@@ -1,9 +1,7 @@
-# PBDL Table of content (cf https://jupyterbook.org/customize/toc.html)
-#
-
-- file: intro.md
-
-- part: Introduction
+format: jb-book
+root: intro.md
+parts:
+- caption: Introduction
   chapters:
   - file: intro-teaser.ipynb
   - file: overview.md
@@ -15,56 +13,41 @@
     sections:
     - file: supervised-airfoils.ipynb
     - file: supervised-discuss.md
-
-- part: Physical Losses
+- caption: Physical Losses
   chapters:
   - file: physicalloss.md
   - file: physicalloss-code.ipynb
   - file: physicalloss-discuss.md
-
-- part: Differentiable Physics
+- caption: Differentiable Physics
   chapters:
   - file: diffphys.md
   - file: diffphys-code-burgers.ipynb
   - file: diffphys-discuss.md
   - file: diffphys-code-ns.ipynb
   - file: diffphys-dpvspinn.md
-
-- part: Complex Examples with DP
+- caption: Complex Examples with DP
   chapters:
   - file: diffphys-examples.md
   - file: diffphys-code-sol.ipynb
   - file: diffphys-control.ipynb
   - file: diffphys-outlook.md
-
-- part: Reinforcement Learning
+- caption: Reinforcement Learning
   chapters:
   - file: reinflearn-intro.md
   - file: reinflearn-code.ipynb
-  
-# - part: Physical Gradients
-#  chapters:
-#  - file: physgrad.md
-#  - file: physgrad-comparison.ipynb
-#  - file: physgrad-nn.md
-#  - file: physgrad-discuss.md
-  
-- part: PBDL and Uncertainty 
+- caption: PBDL and Uncertainty
   chapters:
   - file: bayesian-intro.md
   - file: bayesian-code.ipynb
-
-- part: Fast Forward Topics
+- caption: Fast Forward Topics
   chapters:
   - file: others-intro.md
   - file: others-timeseries.md
   - file: others-GANs.md
   - file: others-lagrangian.md
   - file: others-metrics.md
-  
-- part: End Matter
+- caption: End Matter
   chapters:
   - file: outlook.md
   - file: references.md
   - file: notation.md
-  

overview.md

@@ -2,7 +2,7 @@ Overview
 ============================
 
 The name of this book, _Physics-Based Deep Learning_,
-denotes combiniations of physical modeling and numerical simulations with
+denotes combinations of physical modeling and numerical simulations with
 methods based on artificial neural networks. 
 The general direction of Physics-Based Deep Learning represents a very
 active, quickly growing and exciting field of research, and the following chapter will