Overview
What this challenge is about.
Use SciPy's solve_ivp + SUNDIALS (via scikit-sundae or diffeqpy) to benchmark: RK45 (baseline), LSODA, BDF (CVODE), Radau, and Rosenbrock. Run on three model classes (insulin-glucose 22 states, drug-receptor binding 14 states, hepatic clearance 9 states) at 10,000 parameter samples each. Report success rate, wall time per trajectory, accuracy vs a reference high-precision solution. Deliver a Python benchmark harness, results report, and a 5-page integrator-selection policy memo (model class → recommended integrator + tolerances).
The Brief
What you'll do, and what you'll demonstrate.
Benchmark stiff-aware ODE integrators on three PK/PD model classes and produce an integrator-selection policy that eliminates the current pipeline's failure rate.
Earning criteria — what you'll demonstrate
- Diagnose ODE stiffness using eigenvalue spectra or numerical signatures
- Compare BDF, Rosenbrock, and implicit Runge-Kutta methods on stiff systems
- Choose absolute and relative tolerances defensibly for population workloads
- Build a model-class-to-integrator policy reusable across sponsors
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
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Careers
Roles this prepares you for.
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