1. A method of computing physiological measurements resulting from a multi-scale system using a data-driven model, the method comprising: generating a database of physiological measurements associated with a multi-scale physiological system; using, by a computer, dimensionality reduction techniques on the database to identify a reduced set of components explaining the multi-scale physiological system; learning, by the computer, a data-driven model of the multi-scale physiological system from the database according to a process comprising: receiving a plurality of related physiological measurement values corresponding to times between a start time value and a current time value, selecting a subset of simulated physiological measurement vectors from the database, creating an interpolated related physiological measurement vector based on the subset of simulated physiological measurement vectors and the related physiological measurement values, and projecting the interpolated related physiological measurement vector onto a manifold space to yield a plurality of related physiological measurement manifold coefficients; receiving, by the computer, new input parameters; computing, by the computer, new physiological measurements from the new input parameters based on the plurality of related physiological measurement manifold coefficients, using the data-driven model; computing, by the computer, new derived physiological indicators based on the reduced set of components; and displaying at least one of the new physiological measurements and the new derived physiological indicators.