Where the Unknown Becomes Known.
Lane Vector applies physics-informed neural networks to prediction across domains—replacing scalar heuristics with the governing equations of behavioral and temporal dynamics.
Physics-informed neural networks for non-conservative behavioral systems.
Core innovations
Three structural advances that define the Lane Vector research program.
- Semantic Velocity and Vector KinematicsMany systems reduce behavior to scalar quantities—volume, counts, single metrics. Lane Vector treats observables as vectors in semantic space with velocity, direction, and acceleration. The result is a complete kinematic description of observable dynamics: not just magnitude, but where it is going, how fast, and along what trajectory.
- Stiff-PINN ArchitectureBehavioral dynamics exhibit extreme temporal stiffness—fast events resolve in milliseconds while slow processes evolve over months. Standard neural networks collapse under this timescale separation. Lane Vector adapts Stiff-PINN architectures from computational combustion chemistry, enabling stable integration across six orders of magnitude in temporal dynamics.
- Governing Equations via Kolmogorov-Arnold NetworksMost models approximate. Lane Vector discovers. Using KANs for symbolic regression, the solver identifies closed-form governing equations for dynamic variables that modulate barriers—such as “Market Pressure.” These are not tuned parameters. They are the physics of the system, extracted from data.
What is Lane Vector?
Lane Vector is a Physics-Informed Neural Network research program that applies classical mechanics and chemical kinetics to high-dimensional dynamical systems and behavioral and temporal data. The core insight is structural: observables are not scalar values—they are vectors in a high-dimensional space, possessing magnitude, direction, velocity, and acceleration. By formulating dynamics with Lagrangian mechanics, we turn prediction into a physics problem—one with governing equations, conservation principles, and computable geodesics. Applications span from discovery and intent to emergency room wait times and fraud detection. The project operates through Golden Goose Tools in Tennessee.
The single most communicable correction: value scales with volume and alignment.
Explore
Research thrusts, technology, applications, team, and public resources.
About
Mission, origin story, and research vision.
Research
Four thrusts: Stiff-PINN, KANs, CINN, geodesic mapping.
Technology
From scalars to kinematics: Levels 0–4 and the solver.
Applications
Prediction across domains: ER wait times, fraud, discovery, and more.
Team
Principal Investigator and advisory domains.
Publications
Working papers, conferences, technical reports.
Active Work
Timeline, milestones, and recent deliverables.
Data
Public datasets and format documentation.
Contact
Research inquiries, collaboration, and location.
Secure Portal
Authenticated access to documents, data, and the kinematic tool.