Every major simulation platform -- ANSYS, Siemens, COMSOL, NVIDIA PhysicsNeMo -- uses 200-year-old Fourier physics. We have proven it is fundamentally incomplete. 4.3x predictive accuracy improvement validated on 79,000+ real measurements.
Every major simulation tool assumes diffusivity K is constant. Heat spreads at the same rate regardless of local geometry or structure. This forces addition of complex turbulence models as patches for missing physics.
Requires k-ε, k-ω, LES turbulence models, effective lengths, and dozens of tunable parameters — all patches for missing physics.
Diffusivity D(S) adapts to local thermal structure. High-gradient regions naturally diffuse slower. This produces bi-exponential cooling that matches real physics. Single parameter β replaces turbulence models.
One parameter (β) replaces entire turbulence model zoo. Validated across Weber packed-bed, Sullivan-Thompson rod, and Ilmenau experiments.
4-7x better prediction on unseen data. 46x improvement in transition regions. Validated on 79,000+ real measurements. The improvement shows when you predict, not just fit. This is the true test of physics correctness.
"Fourier and Navier–Stokes are not wrong; they are incomplete in structured regimes. Structured-Entropy restores the missing geometry — and that changes everything for physics simulation and AI digital twins."
See how Structured-Entropy physics captures what Fourier misses in real-time simulation.
SAME PHYSICS: Heat flows through complex channels, not uniformly like Fourier assumes.
The Weber validation transfers directly to chip cooling.
Structured-Entropy physics loss functions replace Fourier-based constraints in neural network training.
Physics-Informed Neural Networks (PINNs) encode physical laws as loss constraints during training. Every existing PINN for thermal simulation uses Fourier's equation as the physics loss. Replacing Fourier with Structured-Entropy gives the neural network better physics to learn from.
The Weber packed-bed experiment is not just academic validation. It is proof that the physics used in every chip thermal simulation today has a fundamental flaw causing 5-20x error in exactly the geometries that matter most: structured channels where heat is hardest to manage.
For an industry spending $50+ billion annually on data center cooling, and facing a thermal wall that limits AI scaling, this is a massive commercial opportunity.
Orbyfy is also a research lab. We are driving foundational research powering the next generation of physics-informed AI. Our Structured-Entropy Physics framework proves that 200-year-old Fourier-Navier-Stokes equations are fundamentally incomplete—delivering 4-7x predictive accuracy improvements over classical physics, with peaks up to 46x in transition regions. We're powering the future of autonomous digital twins.
Orbyfy Labs Research
Orbyfy Labs Research
Orbyfy Labs Research
Orbyfy Labs Research
Orbyfy Labs Research
Based on Orbyfy Research (2025) -- Structured-Entropy Physics: A new framework for thermal transport in structured geometries. Published on ResearchGate with full experimental validation data.
Validated on Weber Packed-Bed Experiment: 79,000+ measurements | 9 experiments | 36 sensors