Relativity and Quantum Simulation Development Platform
A research workbench that helps you develop scientific workflows, understand results through ISAES, and build a simulation environment of your own—with an optional AI Research Assistant.
For academic research & corporate R&D
01 / Your AI Research Assistant
Use natural language as the liaison between your scientific intent and the code. PF brings the conversation into your simulation workflow—with evidence you can inspect and changes you control.
r34 SaaS walkthrough · Narrated · English captions
A walkthrough of the research workflow with scripted AI replies and real numerical execution. Storage setup is shown.
“Trace the receiver-mode assumptions behind this result. Help me test a different model in my own simulation stack.”
An illustrative research prompt
Explore the assistant guideAsk about a completed run. Examine its inputs, numerical arrays, plots, logs and permitted scientific source.
Develop a private Python layer with an explanation and a code diff you can review.
Execute baseline and candidate stacks, then inspect numerical comparisons and reference checks.
Explicitly activate a passing layer and use your ordered stack in new simulations.
02 / Your proprietary subsystem
Go beyond a fixed configuration. Compose modular enhancements that override permitted scientific Python implementations for your research, while the shared installation remains intact.
Illustrative stack organization · Select a layer to explore
Propose and test a different scientific implementation, such as a receiver-mode assumption. Private source overlays can change permitted equations and behavior as well as numerical settings.
Intelligent Scientific Analysis and Evidence System
ISAES connects retained results to eligible scientific methods, explicit checks, linked figures and consistent presentations. Much of it runs under the hood: preserving data meaning, checking prerequisites, tracing findings and keeping scientific support separate from a successful export.
Intelligent Scientific Analysis and Evidence System · Narrated · English captions
A narrated explanation and walkthrough of retained scientific results, method eligibility, evidence, linked views, presentation exports and researcher controls. Actual workbench captures and recorded local calculations are identified separately from explanatory diagrams and scripted assistant replies. External qualification boundaries remain explicit.
Uncertainty, comparisons, classical learning and bounded quantum evidence use declared meaning and assumptions.
Follow exact retained values through the method, finding, linked visualization and exported presentation.
Generate and explore reports without an AI provider. Optional report discussion uses explicit review; autonomous investigations use a separate standing policy with selected permissions and budgets.
Reusable private workflows
Bring a Python program, or use the workflow assistant to build one from an explicit mathematical specification. Review the code, prepare reusable GUI controls, validate the calculations and explicitly activate the workflow. Return to the saved form, change settings and retain each version’s results.
Private workflows: GUI and server walkthrough · Narrated · English captions
Learn why the Python program is needed, how its model and outputs are built, and how to author it with the workflow assistant or the development guide. Then follow the custom Qiskit study through validation, reusable controls, private versions and results, with B2/B3 server checks. Real calculations use the designated developer executor; AI replies are scripted. Production container isolation is outside this recording’s scope.
API-agnostic Python adapters support compatible installed libraries. Project permissions, AI disclosure policy and the isolated runtime apply to the workflow lifecycle.
03 / Research under your control
Build a research environment around your institution’s requirements. Restricted projects, institutional AI policy, private layers, BYOK and optional BYOS give you controls over access, disclosure and retained evidence.
Connect your own credentials for OpenAI, compatible endpoints, DeepSeek, Google Gemini or Anthropic / Claude. Keep provider billing on your own account, within your organization’s and project’s approved AI policy.
Connect an S3-compatible destination or an outbound NAS / folder connector. Retain large inputs, results and evidence in the location your research requires.
PF retains authorization, conversations, layer records and trusted hashes. Selected evidence goes to your configured AI provider only when effective policy and any required consent allow it. Understand the data boundaries
04 / Inside the simulation
Gauge-wave fields, convergence and a numerical photon link.
Published orbital inputs, propagated trajectories and a matched model comparison.
Compare exact quantum-state predictions with simulated measurement estimates.
Generated with r34 numerical solvers. Every gallery study includes its configuration or reproduction bundle, method and interpretation.
A connected research workbench
Connect specialist models to a research question. Keep each study’s inputs, assumptions and numerical evidence visible as you develop your own private workflow.
Propagate published orbital elements at current UTC times and connect predicted trajectories to photon timing, visibility and quantum-link models.
Explore satellite researchCompare local quantum simulations, state reconstruction and compatible companion studies through the implemented scientific integration routes.
Explore the integrationsSatellite tracking uses periodically refreshed orbit predictions. Gallery examples are recorded numerical studies, with their configurations and interpretation available.
05 / An inspectable scientific foundation
Connect curated scientific references, explicit numerical checks and optional scientific integrations. PF Simulation r34 provides direct CLI source and input provenance, a complete Linux hosting reference, restricted projects and institutional AI disclosure controls.
Explore deployment and research controlsRepeated test identities count once in the headline. The numerical ledger covers 46 declared studies. Subtests, benchmark assertions and interface checks keep their own units.
The latest Assistant consolidation passed 305 targeted Python tests and 232 UI fixture cases across 14 commands. It brings the approved recovery, saved-conversation loading and opening fixes into the shared standalone/SaaS release. Subtests and assertion checks remain separate; no simulation-result cache is introduced. The autonomous release and upload maintenance are included in the reconciled ledger. Full regression, scientific and performance evidence remains bound to the source on which it ran.
Explore the totals, highlights and counting method