Update course structure and module descriptions for clarity and consistency

Author: Daniel Precioso, PhD

index.qmd

@@ -16,7 +16,7 @@ Quick links:
 
 ## Course at a glance
 
-- **Format:** 10 live in-person sessions
+- **Format:** 8 core modules plus 1 extra Lorenz module
 - **Tools:** Python, NumPy, SciPy, matplotlib, Streamlit, NetworkX
 - **Main goal:** learn modeling by building simulations you can explore and explain
 
@@ -27,16 +27,31 @@ Quick links:
 - **Streamlit app** (interactive demos): `streamlit/`
 - **Datasets** used in network sessions: `data/`
 
+## Course map
+
+- [ODEs in 1D](modules/ode-1d/)
+- [ODEs in 2D](modules/ode-2d/)
+- [Coupled ODEs](modules/ode-coupled/)
+- [Collective Motion](modules/collective-motion/)
+- [Networks](modules/networks/)
+- [Partial Differential Equations](modules/pde/)
+- [Cellular Automata](modules/cellular-automata/)
+- [Agent-Based Modeling](modules/agent-based-modeling/)
+- [Extra: Lorenz Attractor](modules/lorenz/)
+
 ## What you will build
 
 Across the course you will implement and experiment with:
 
-- **ODE models** (SIR, spruce budworm, Michaelis–Menten)
-- **Nonlinear oscillators** (Van der Pol, FitzHugh–Nagumo)
-- **Reaction–diffusion PDEs** (finite differences + animations)
-- **Collective motion** (Vicsek collective motion + interaction)
-- **Networks** (metrics, spreading processes, real datasets)
-- **Cellular automata** (1D rules + traffic models)
+- **One-dimensional ODE models** (SIR, spruce budworm, Michaelis-Menten)
+- **Two-dimensional oscillators** (CDIMA, Van der Pol, FitzHugh-Nagumo)
+- **Coupled oscillator simulations** (Kuramoto synchronization)
+- **Collective motion models** (Vicsek, Couzin, predator response)
+- **Network analysis and spreading simulations** (metrics, graph models, SIS/SIR)
+- **Reaction-diffusion PDE solvers** (Gierer-Meinhardt, Gray-Scott)
+- **Cellular automata experiments** (1D rules and rule exploration)
+- **Agent-based traffic models** (flow, congestion, density sweeps)
+- **A deterministic chaos case study** (Lorenz attractor)
 
 ## Getting started locally
 

syllabus.qmd

@@ -6,44 +6,42 @@ format: html
 
 ## Program
 
-This course has **10 live in-person sessions**.
+This site is organized around **eight core modules** plus one extra Lorenz attractor module.
 
-### 1D ODEs (SciPy + Streamlit)
+The sidebar navigation is the ground-truth order for the material, and this page follows the same structure.
 
-Simulate classical one-dimensional ODE models (SIR epidemiological model, spruce budworm population model, Michaelis–Menten enzyme kinetics). Solve ODEs numerically with SciPy in Python, and build/deploy a simple Streamlit web app to explore parameter effects. Groups are assigned and remain for the whole course.
+### ODEs in 1D
 
-### 2D ODEs (Nonlinear Oscillators)
+Build the first simulation workflow with one-dimensional ODEs. You solve IVPs with `scipy.integrate.solve_ivp`, compare trajectories, and interpret parameter changes through the SIR epidemic model, Michaelis-Menten kinetics, and the spruce budworm model.
 
-Explore two-dimensional ODEs via nonlinear oscillatory systems: Van der Pol oscillator and FitzHugh–Nagumo model. Create animations with matplotlib and build interactive Python programs that let users set initial conditions via mouse clicks.
+### ODEs in 2D
 
-### Coupled ODEs (Kuramoto Model)
+Move from scalar models to planar dynamics. You work with the CDIMA reaction, the Van der Pol oscillator, and the FitzHugh-Nagumo model, then animate trajectories and phase portraits to study oscillations and excitability.
 
-Implement coupled ODEs, focusing on the Kuramoto model. Animate multiple plots simultaneously (e.g., oscillator evolution and a bifurcation diagram).
+### Coupled ODEs
 
-### Collective Motion (Vicsek Model)
+Study synchronization through the Kuramoto model. You simulate many interacting oscillators, track order parameters, and connect time-domain behavior with summary diagrams.
 
-Simulate flocking behavior using the Vicsek model. Implement interaction rules for “boids” and extend the simulation by treating the mouse as a predator and coding avoidance behavior.
+### Collective Motion
 
-### Networks I (NetworkX Fundamentals)
+Model flocking and alignment in continuous space. The main case studies use Vicsek and Couzin-style rules, then extend them with interactive animation and predator avoidance.
 
-Introduce NetworkX: build graphs, compute structural metrics (degree distribution, clustering coefficient, centrality), and visualize different network types. Establish foundations for modeling dynamics on networks.
+### Networks
 
-### PDEs via Reaction-Diffusion Systems
+Represent systems as graphs with `networkx`. You measure connectivity and centrality, generate standard graph models, and simulate SIS and SIR spreading on synthetic and real networks.
 
-Introduce partial differential equations through reaction-diffusion models (Gierer-Meinhardt and Gray-Scott). Implement 1D and 2D Laplacians with NumPy and animate spatiotemporal evolution to study Turing instability and pattern formation.
+### Partial Differential Equations
 
-### Networks II (Spreading on Real Networks)
+Discretize reaction-diffusion systems in one and two spatial dimensions. You build Laplacians with finite differences, study Turing instability, and simulate Gierer-Meinhardt and Gray-Scott pattern formation.
 
-Simulate spreading processes (fake news, epidemics) on real-world networks. Retrieve and process open-source network datasets, and investigate how network structure shapes propagation dynamics.
+### Cellular Automata
 
-### Cellular Automata I (1D CA)
+Switch from continuous fields to fully discrete local rules. You implement one-dimensional cellular automata, build space-time diagrams, and compare how rule choice and initial conditions affect long-run behavior.
 
-Introduce one-dimensional cellular automata as a framework for discrete dynamical systems. Explore deterministic and stochastic CA, and how simple local rules generate complex global behavior.
+### Agent-Based Modeling
 
-### Cellular Automata II (Traffic Dynamics)
+Use traffic as the main agent-based case study. You implement the Nagel-Schreckenberg model, visualize stop-and-go waves, and measure density, flow, and congestion.
 
-Apply cellular automata to traffic modeling with the Nagel–Schreckenberg model. Study congestion, flow, and phase transitions by tuning parameters such as vehicle density and maximum speed.
+### Extra: Lorenz Attractor
 
-### Final Project Support
-
-Wrap-up and support session for the final project: address remaining questions, clarify concepts, and help groups prepare deliverables.
+Extend the ODE material into deterministic chaos. You simulate the Lorenz system, plot the attractor in three dimensions, and measure sensitivity to initial conditions.