To discretize a continuous diffusion process effectively, start by choosing a numerical method that balances accuracy, stability, and computational efficiency. The most common approach involves breaking the continuous process into discrete time steps and approximating the spatial derivatives or stochastic terms. For deterministic diffusion (e.g., heat equations), finite difference methods are widely used. For stochastic diffusion (e.g., Brownian motion), techniques like the Euler-Maruyama method adapt deterministic solvers to handle randomness. The key is to ensure the discretization preserves essential properties of the original process, such as conservation laws or statistical behavior.
For example, consider the heat equation ∂u/∂t = α∇²u. Using a finite difference approach, discretize time into steps Δt and space into a grid with spacing Δx. Replace derivatives with approximations: ∂u/∂t ≈ (uⁿ⁺¹ − uⁿ)/Δt and ∇²u ≈ (uⁿ₊₁ − 2uⁿ + uⁿ₋₁)/Δx². This yields an explicit update rule: uⁿ⁺¹ = uⁿ + (αΔt/Δx²)(uⁿ₊₁ − 2uⁿ + uⁿ₋₁). For stochastic processes like dXₜ = μ(Xₜ)dt + σ(Xₜ)dWₜ, the Euler-Maruyama method approximates each step as Xₙ₊₁ = Xₙ + μ(Xₙ)Δt + σ(Xₙ)√Δt⋅Zₙ, where Zₙ is a standard normal random variable. This captures both drift and diffusion terms incrementally.
Practical considerations include stability conditions and error control. For explicit methods like the heat equation example, the Courant-Friedrichs-Lewy (CFL) condition (αΔt/Δx² ≤ 0.5) must be satisfied to avoid numerical instability. Implicit methods (e.g., Crank-Nicolson) relax stability constraints but require solving linear systems. For stochastic processes, smaller time steps reduce discretization error but increase computation. Adaptive step sizing or higher-order methods (e.g., Milstein) can improve accuracy for stiff systems. Libraries like SciPy (for PDEs) or specialized SDE solvers in frameworks like TensorFlow Probability provide tested implementations to avoid common pitfalls like overshooting or undersampling random increments.