Mathematical Simulations

Dynamical Systems • Geometry • Chaos • Visualization

Numerical explorations of complex mathematical phenomena: differential equations, geometric transformations, and chaotic behaviors.

Copula Laboratory

Probability • Dependence Structures • Financial & Climate Risk

Interactive

A copula separates the dependence between variables from their marginal distributions — this is Sklar's theorem. This laboratory simulates five families (Gaussian, Student-t, Clayton, Gumbel, Frank) in real time: vary the parameter θ and watch the dependence structure morph while the marginals stay fixed. Every empirical measure (Kendall's τ, Spearman's ρ, tail dependence λL, λU) is compared against its closed-form theoretical value.

Sklar's Theorem

F(x, y) = C( F_X(x) , F_Y(y) )

λ_L = lim_{u→0⁺}  P( U₂ ≤ u | U₁ ≤ u )      (lower-tail dependence)
λ_U = lim_{u→1⁻}  P( U₂ > u | U₁ > u )      (upper-tail dependence)
τ   = 4 ∬ C(u,v) dC(u,v) − 1                (Kendall's tau)

Applied Scenarios

Financial contagion Clayton — lower-tail dependence: assets crash together (market crashes).
Climate co-extremes Gumbel — upper tail: joint heatwaves & droughts. Directly tied to agroclimatic modeling.
The Gaussian trap λ → 0 in theory, yet visible at a finite threshold: the lesson of the 2008 crisis.
Elliptical tails Student-t — symmetric tail dependence, controlled by the degrees of freedom ν.

Technique: simulation via conditional / inversion methods (Marshall-Olkin for Archimedean families, Cholesky decomposition for elliptical ones), numerically computed inverse CDFs for the marginals, additive-blending Canvas rendering. No external dependencies, single file. Empirical measures vs. closed forms from Cherubini, Durante & Mulinacci, Principles of Copula Theory.

Open fullscreen → Technical articles →

GPS Trajectory — Cinematic Visualization

Trajectories • Kinematics • Data Visualization

New

Cinematic visualization of a GPS trajectory using time-based interpolation, smoothing techniques, and dynamic camera motion to reveal the underlying kinematic structure of the path.

Coming next: velocity-based coloring, temporal annotations, and interactive exploration of the trajectory.

Point Cloud Lab

3D • Projections & Spherical • LiDAR / Vision

Nouveau

Interactive exploration of point clouds: 3D visualization, orthographic (top-down) and spherical projection type panorama. Demonstration of a 3D → 2D pipeline used in computer vision, photogrammetry, and LiDAR.

Polyhedra Morphing

Geometry • Transformations • Interpolation

New

A visual exploration of geometric transitions between regular and semi-regular polyhedra. The simulation demonstrates continuous interpolations between different polyhedral structures, revealing underlying geometric relationships.

Included Polyhedra

Tetrahedron 4 triangular faces
Octahedron 8 triangular faces
Icosahedron 20 triangular faces
Cube 6 square faces
Dodecahedron 12 pentagonal faces

Technique: Linear interpolation of vertex coordinates with normalization to maintain scale. Continuous rotation highlights the symmetry of each shape.

More visualizations → Technical articles →

Lorenz Attractor

Deterministic Chaos • Sensitivity to Initial Conditions

The Lorenz system is a classic example of deterministic chaos. Despite perfectly deterministic evolution rules, the system exhibits extreme sensitivity to initial conditions. The video shows the 3D trajectory and its projections onto canonical planes.

System Equations

dx/dt = σ (y − x)
dy/dt = x (ρ − z) − y
dz/dt = x y − β z
σ = 10 Prandtl number
ρ = 28 Rayleigh number
β = 8/3 Geometric parameter

Coming soon: Visualization of exponential divergence of nearby trajectories, fractal dimension of the attractor, and coloring by time or local velocity.

Nonlinear Damped Pendulum

Differential Equation • Energy Dissipation

Simulation of a rigid pendulum under gravity with linear damping. The sin(θ) term introduces a nonlinearity that distinguishes the real behavior from the small-angle harmonic approximation.

Equation of Motion

d²θ/dt² + a dθ/dt + (g/ℓ) sin(θ) = 0

Where θ is the angle from vertical, a is the damping coefficient, g is gravitational acceleration, and is the pendulum length.

Coming soon: Energy graphs E(t), phase portrait (θ, dθ/dt), and comparison with the linearized solution to highlight nonlinear effects.

Methodology

Each simulation combines rigorous mathematical modeling with optimized visual rendering to reveal the underlying structure of the studied phenomena.

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Modeling

Precise mathematical formulation (ODE, PDE, geometric transformations). Phase space study and stability analysis.

Numerical Computation

Integration with adaptive methods (RK4, RK45) and optimized time step. Validation through conservation of invariants.

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Visualization

3D rendering with dynamic camera, fluid trajectories, and multiple projections. High-quality export for web and presentation.

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Analysis

Geometric and physical interpretation of results. Complete documentation of parameters and assumptions.

Next step: Development of an interactive interface allowing real-time parameter adjustment and guided exploration of the solution space.

Contact

📧 salemnknd@gmail.com

📍 Québec, Canada