Applied Mathematics

Learn Mathematical Modeling

Turning real questions into mathematics and checking the answer: the modeling cycle and dimensional analysis (units, the Buckingham-pi idea, sanity-checking equations by units), rate/compartment models with exponential and logistic growth, optimization modeling (setting up and solving a real max/min such as the minimal-cost can), discrete difference-equation models and their equilibria, the stability of fixed points (whether a perturbation is damped or amplified), and model validation with residuals, over-fitting, and parameter sensitivity.

Free to start · adaptive placement finds your level · reviews timed to your own forgetting.

What you'll learn

30 lessons in Mathematical Modeling

The modeling cycle & dimensional analysisRate models — exponential & logistic growthOptimization modeling — the minimal-cost canDiscrete models — difference equationsEquilibria & stabilityModel validation & sensitivityMonte Carlo — estimating with randomnessLeast squares — fitting a line to dataScaling laws — the square-cube ruleFermi estimationCompartment & mixing modelsDimensionless groups & the Pi theoremPower laws & log-log plotsInterpolation, extrapolation & where models breakConstrained optimization & feasible regionsExpected value & decision modelsFeedback loopsAgent-based & cellular-automaton modelsStocks and flowsDelay in a modelFitting a dynamic model to dataPractical identifiabilityChoosing an input distributionUncertainty propagationVariance-based global sensitivityDesigning simulation runsStructural uncertainty & ensemblesHindcasting & tuning to historyDiscrete-event simulationWhat validation establishes
How Erudia teaches

Built to be understood — and remembered.

Every idea is taught with motivation and a worked example before the drills, and an FSRS spaced-repetition engine schedules each review for the day its model predicts you would forget it. A short placement check finds what you already know, so you start Mathematical Modeling exactly where it's useful.

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