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.

A simulation that couples several fields often calls several mathematics libraries and translates between their conventions. Unified math tries to remove that translation. Seventeen open-source crates rest on one hierarchy of algebraic traits and one interface from category theory, and a program sets its numerical precision in one line. The work is ongoing: how much of mathematics composes this way is not yet known.

A simulation answers one question per run. Asking a second one costs a second run, and the two share no history; each is rebuilt from the start, or from a checkpoint fixed before either began. Counterfactual CFD branches instead. The run pauses at a state the physics reaches; from there every candidate continues, and the outcomes are scored against stated limits in a single table. Flow, reacting chemistry, navigation, and control step the same field in the same process, so an interaction between any two of them reaches the answer instead of being lost where two codes meet. The work is Counterfactual Fluid Dynamics.

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.