Table 2. Comparison of employed parameter estimation methods.

Method	Advantages	Disadvantages
Derivative-based	Very efficient for convex functions	Sensitive to starting point; trapped by local maxima; sensitive to noise
Differential evolution/random forests	Insensitive to starting point; able to identify global maxima in complex landscapes; reports multiple high scoring solutions; less sensitive to noise; easily parallelizable; less computationally expensive	Inefficient for simple, convex functions
Model reduction	Efficient for computationally expensive models; reduced model has clear physical interpretation	Requires high fidelity reduced model; no general procedure for model reduction
Statistical surrogate	Efficient for computationally expensive models; surrogate can be constructed automatically	Many model evaluations required to construct surrogate; surrogate has no physical interpretation