Abstract: A set of conditions is defined that to be simulated via execution of a machine-learning model. For each condition, a set of learnable condition-specific parameters is identified to configure a model architecture. A first learnable condition-specific parameter associated with a first condition of the set of conditions can be identified a shared or global parameter that is to have a same value as at least another learnable condition-specific parameter (associated with another condition). One or more parameter data structures can be configured with parameter values for the sets of condition-specific parameters for the sets of conditions, where the configuration imposes a constraint that a value for the first condition-specific parameter and the at least one value for the at least one other condition-specific parameter are the same. The machine-learning model can be trained using the configured parameter data structure(s).