Our proposed effort is designed to address zero knowledge inference: the task of finding mod-
els from data when we do not necessarily know what the correct model structure is. Absence of
such prior knowledge is becoming increasingly common for the complex questions we are now
asking in biology, social systems, physics and engineering. Thus, we cannot reduce this exercise to one of simply tuning parameters, and/or model calibration. Sparsity of reported de novo modeling paradigms, that allow automated abduction of good models from raw data is evidently a key bottleneck in automated problem solving. Our effort is designed to address this emerging gap.

FP064753-01-PR-CR-01

2017-06-29

2021-06-29