When a Turing Pattern Has to Grow with Its Domain
A verified reaction–diffusion experiment asks when tissue growth preserves a spatial pattern and when it forces peaks to split, merge, or disappear.
Read articleKenneth Cheng · Macau
From pattern formation and nonlinear mechanics to scientific institutions, reliability, and experimental design: reproducible computation, explicit assumptions, and honest limits turn models into testable research questions.
Latest writing
Every research article is manually written from verified private ScienceProject results and includes three approved figures, numerical checks, negative findings, claim boundaries, and references.
A verified reaction–diffusion experiment asks when tissue growth preserves a spatial pattern and when it forces peaks to split, merge, or disappear.
Read articleA reduced morphoelastic beam experiment tests whether small spatial changes in stiffness and growth load can steer fold number and location without increasing peak curvature.
Read articleA synthetic exothermic-reactor study quantifies how much nominal production is surrendered when a decision must remain below a thermal-risk criterion under uncertain kinetics and heat transfer.
Read articleResearch map
Research is organized by questions and methods, not only by publication date.
Spatial disease, ecosystems, coastal risk, climate forcing, and energy-environment interactions.
Pathways from an open-ended question to assumptions, computation, critique, and communication.
Sensitivity, calibration, robustness, and decision-making when model inputs or operating conditions are uncertain.
Models of routing, accessibility, traffic stability, fleet operations, and infrastructure trade-offs.
Selected research
Only projects with a clear question, reviewed figures, and visible assumptions and limitations appear here.

A cohort-aware model of how slopes, stairs, transfers, crowding, and missing elevators change routes through a border hub.
A calibrated graph-CUSUM benchmark for warning of local battery-pack thermal faults when a temperature sensor drifts or becomes unavailable.
Known physical laws form a controlled benchmark for unit-aware monomial symbolic regression.
Teaching
Courses, interactive models, Python laboratories, and mentoring form a path from first assumptions to independent inquiry.