  A multi-physics formulation for Data Driven Prognosis (DDP) is developed.
Unlike traditional predictive strategies that require controlled off-line
measurements or training for determination of constitutive parameters to derive
the transitional statistics, the proposed DDP algorithm relies solely on in
situ measurements. It utilizes a deterministic mechanics framework, but the
stochastic nature of the solution arises naturally from the underlying
assumptions regarding the order of the conservation potential as well as the
number of dimensions involved. The proposed DDP scheme is capable of predicting
onset of instabilities. Since the need for off-line testing (or training) is
obviated, it can be easily implemented for systems where such a priori testing
is difficult or even impossible to conduct. The prognosis capability is
demonstrated here via a balloon burst experiment where the instability is
predicted utilizing only on-line visual observations. The DDP scheme never
failed to predict the incipient failure, and no false positives were issued.
The DDP algorithm is applicable to others types of datasets. Time horizons of
DDP predictions can be adjusted by using memory over different time windows.
Thus, a big dataset can be parsed in time to make a range of predictions over
varying time horizons.
