After a Good Fit, What Should We Measure?
A short note on choosing the next observation when several parameter explanations already fit the data.
Topic
Learning, mentoring, evidence and research design.
A short note on choosing the next observation when several parameter explanations already fit the data.
A failed check can narrow a useful question. Two published numerical studies show why the original failure must remain visible.
A five-station study asks whether solar prediction intervals remain trustworthy during rapid power changes and missing sensor inputs. Better calibration improves every station, yet the hardest groups still fall far below the intended coverage.
A stiff transfer-network benchmark compares standard explicit integrators with a linearly implicit update that preserves non-negativity and total mass.
A controlled symbolic-regression benchmark asks whether dimensional constraints improve exact equation recovery, shrink the search space, and prevent unit-fragile formulas.
A compact workflow for assumptions, model choice, computation, validation, limitations, and communication.
Pathways from an open-ended question to assumptions, computation, critique, and communication.
2026
Known physical laws form a controlled benchmark for unit-aware monomial symbolic regression.
2026
A shared-capacity predictive-control benchmark for classroom CO₂ exposure when actual room use departs from the timetable.
2026
A stiff transfer-network benchmark compares explicit methods with a positivity- and mass-preserving linearly implicit update.
2026
A teaching case combines sea-level scenarios, extreme-value storm surges, and cost optimization over a 50-year horizon.
2026
An accepted ADMA 2026 short paper on conditional solar prediction-interval reliability. Five-station comparisons distinguish consistent calibration improvement from the absolute reliability that difficult events still lack.