Model-free Data-Driven Inference
We present a model-free data-driven inference method that enables inferences on system outcomes to be derived directly from empirical data without the need for intervening modeling of any type, be it modeling of a material law or modeling of a prior distribution of material states. We specifically consider physical systems with states characterized by points in a phase space determined by the governing field equations. We assume that the system is characterized by two likelihood measures: one $μ_D$ measuring the likelihood of observing a material state in phase space; and another $μ_E$ measuring the likelihood of states satisfying the field equations, possibly under random actuation. We introduce a notion of intersection between measures which can be interpreted to quantify the likelihood of system outcomes. We provide conditions under which the intersection can be characterized as the athermal limit $μ_\infty$ of entropic regularizations $μ_β$, or thermalizations, of the product measure $μ= μ_D\times μ_E$ as $β\to +\infty$. We also supply conditions under which $μ_\infty$ can be obtained as the athermal limit of carefully thermalized $(μ_{h,β_h})$ sequences of empirical data sets $(μ_h)$ approximating weakly an unknown likelihood function $μ$. In particular, we find that the cooling sequence $β_h \to +\infty$ must be slow enough, corresponding to quenching, in order for the proper limit $μ_\infty$ to be delivered. Finally, we derive explicit analytic expressions for expectations $\mathbb{E}[f]$ of outcomes $f$ that are explicit in the data, thus demonstrating the feasibility of the model-free data-driven paradigm as regards making convergent inferences directly from the data without recourse to intermediate modeling steps.