Before writing a single line, define what success looks like in observable behaviors: estimating parameters, recognizing patterns, or justifying claims with evidence produced inside the simulation. Draft quick assessments aligned to those outcomes, and design controls that nudge learners to explore relevant states. This alignment protects scope, guides interface choices, and ensures your eventual performance optimizations actually serve understanding rather than spectacle for spectacle’s sake.
Pick formulations that are stable at interactive timesteps and transparent enough to explain. Simple integrators like semi‑implicit Euler or Verlet may be perfect for mechanics, while Runge‑Kutta variants suit stiffer systems. When moving heavy lifting to compute shaders or WebAssembly loops, validate against known results and include a toggle for slower, higher‑accuracy steps so learners can feel how numerical choices shape conclusions without drowning in theory before curiosity sparks.
Prototype the smallest path from input to meaningful output: a single slider feeding one equation, pushing an array through WebAssembly, then painting pixels with WebGPU. Show it to a learner within a day and watch attentively. Where do eyes linger? Which control invites play? Trim what distracts, instrument what confuses, and keep versioned checkpoints to compare changes. Early momentum builds trust, and each tiny win reduces risk as complexity grows.
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