Swap lecture 10 and 11

.github/workflows/documentation.yml

@@ -17,7 +17,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@v2
         with:
-          version: '1'
+          version: '1.11'
       - uses: julia-actions/cache@v2
       - name: Install dependencies
         run: julia --project=docs/ -e 'using Pkg; Pkg.instantiate()'

README.md

@@ -54,8 +54,8 @@ All interactions will happen **only through GitHub** — no in-person hints will
 | 7  | 10/03/2025   | Lecture - Jouke van Westrenen | Stochastic optimal control, Linear Quadratic Gaussian (LQG), Kalman filtering, robust control under uncertainty, unscented optimal control; | |
 | 8  | 10/10/2025   | Lecture - Kevin Wu | Distributed optimal control & multi-agent coordination; Consensus, distributed MPC, and optimization over graphs (ADMM) ||
 | 9  | 10/17/2025   | **External seminar 4** — François Pacaud | GPU-accelerated optimal control | |
-|10  | 10/24/2025   | Lecture - Michael Klamkin | Physics-Informed Neural Networks (PINNs): formulation & pitfalls | |
-|11  | 10/31/2025   | **External seminar 5** - Chris Rackauckas | Neural Differential Equations: PINNs + classical solvers | |
+|10  | 10/24/2025   | **External seminar 5** - Chris Rackauckas | Neural Differential Equations: classical solvers + adjoint methods | |
+|11  | 10/31/2025   | Lecture - Michael Klamkin | Physics-Informed Neural Networks (PINNs): formulation & pitfalls | |
 |12  | 11/07/2025   | Lecture - Pedro Paulo | Neural operators (FNO, Galerkin Transformer); large-scale surrogates | |
 |13  | 11/14/2025   | **External seminar 6** - Charlelie Laurent | Scalable PINNs / neural operators; CFD & weather applications | |
 |14  | 11/21/2025   | Lecture - TBD | TBD from the pool | |

class10/background_materials/README.md

@@ -1 +1 @@
-# Class 10 Background Material - 10/24/2025 
+# Class 11 Background Material - 10/31/2025

class10/class10.md

@@ -1,10 +1,10 @@
 # Class 10 — 10/24/2025
 
-**Presenter:** Michael Klamkin
-
-**Topic:** Physics-Informed Neural Networks (PINNs): formulation & pitfalls
+**Presenter:** Chris Rackauckas
+**Liason:** Michael Klamkin
 
+**Topic:** Neural Differential Equations: classical solvers + adjoint methods
 ---
 
-Add notes, links, and resources below.
+https://book.sciml.ai/notes/11-Differentiable_Programming_and_Neural_Differential_Equations/
 

class11/background_materials/README.md

@@ -1 +1 @@
-# Class 11 Background Material - 10/31/2025
+# Class 10 Background Material - 10/24/2025 

class11/class11.md

@@ -1,9 +1,9 @@
 # Class 11 — 10/31/2025
 
-**Presenter:** Chris Rackauckas
-**Liason:** Michael Klamkin
+**Presenter:** Michael Klamkin
+
+**Topic:** Physics-Informed Neural Networks (PINNs): formulation & pitfalls
 
-**Topic:** Neural Differential Equations: PINNs + classical solvers.
 ---
 
 Add notes, links, and resources below.