ALAMO is a computational methodology for leaning algebraic functions from
data. Given a data set, the approach begins by building a low-complexity,
linear model composed of explicit non-linear transformations of the independent
variables. Linear combinations of these non-linear transformations allow a
linear model to better approximate complex behavior observed in real processes.
The model is refined, as additional data are obtained in an adaptive fashion
through error maximization sampling using derivative-free optimization. Models
built using ALAMO can enforce constraints on the response variables to
incorporate first-principles knowledge. The ability of ALAMO to generate simple
and accurate models for a number of reaction problems is demonstrated. The
error maximization sampling is compared with Latin hypercube designs to
demonstrate its sampling efficiency. ALAMO's constrained regression methodology
is used to further refine concentration models, resulting in models that
perform better on validation data and satisfy upper and lower bounds placed on
model outputs.

ALAMO 是一种用于从数据中学习代数函数的计算方法，通过从数据中构建低复杂度的线性模型，并使用非线性变换，将线性模型精确逼近实际过程中的复杂行为，并使用无导数优化的误差最大化采样逐步细化模型。同时，该方法可以强制约束反应变量，以纳入基础知识，并用于进一步完善浓度模型，生成表现更好且满足模型输出上下限约束的模型。