Deep SIML Labs
Independent research connecting simulation, optimization, and the physical systems that make intelligence possible.
Building questions into systems
Deep SIML Labs is Richard's independent research program at the intersection of simulation, optimization, and scientific computing. The work asks how systems learn, adapt, and make decisions, and how computational models can eventually connect to physical experiments and new materials.
Richard leads the engineering and research program and publishes its work. His engineering work spans Rust and Python pipelines, a three-dimensional simulation stack, and research around the Free Energy Principle. The public archive brings those directions into individual papers, prototypes, technical notes, and code releases.
A research record that can change
Current work includes dynamic causal geometry, latent planning experiments, and the Stardate SIML prototype. Earlier artificial-life framing and the EranaOS exploration remain accessible with historical or archived status.
That distinction matters: the archive is a record of questions and results over time. A prototype, a philosophical text, and a technical preprint each have a different role in the work.
The Operator
Richard is also The Operator, the name attached to the SIML Codex. The Codex provides a philosophical thread; the technical publications retain their own bylines and evidence standards.
SOURCES & FURTHER EXPLORATION