Research areas

The Effect Propagation Process treats causality as a spacetime-agnostic functional dependency in which one moment of a system arises from a previous moment through a transforming function. The classical static definition is recovered as a specialization. DeepCausality subsumes Pearl's structural causal models, Granger causality, dynamic Bayesian networks, the Rubin Causal Model, and CATE inference, and extends to dynamic causality as required for advanced science and engineering.

Where general relativity computes time dilation from a known gravitational source, the chronodynamic program inverts the question and recovers the source from the observed time dilation. The first concrete result is the recovery of the geocentric gravitational constant GM directly from Galileo satellite atomic-clock data, to a median precision of 4 × 10⁻⁷.

Where the EPP propagates causal effects through a dynamic context, Deep Brain propagates knowledge through a dynamically evolving context. It investigates how a knowledge corpus can be represented as a continuously evolving process rather than as the discrete static snapshot assumed by current database systems — a problem that becomes acute in long-running research workflows.