StrataPhysics replaces 72-hour supercomputing CFD meshes with continuous Fourier Neural Operators. We compute 3D Navier-Stokes, shockwave discontinuities, and non-equilibrium plasma thermodynamics in 16 milliseconds directly on high-throughput accelerated tensor clusters.
# StrataPhysics Fourier Neural Operator Inference
from strataphysics import NeuralPDESolver, MeshFreeGeometry
# Load 70B parameter aerospace foundation world model
solver = NeuralPDESolver.from_pretrained("strata-fno-aero-70b", precision="fp8")
# Ingest continuous geometry and freestream conditions
boundary = MeshFreeGeometry.load("supercritical_wing_v4.step")
flow_solution = solver.solve_navier_stokes(
geometry=boundary,
mach=0.85,
reynolds=1.4e7,
angle_of_attack_deg=2.5,
output_fields=["pressure_coef", "skin_friction", "mach_number"]
)
print(f"Full 3D Pressure Field Converged in {flow_solution.latency_ms:.2f}ms")
flow_solution.export_h5("flow_field_solution.h5")Traditional finite-volume methods spend 80% of human and compute time generating body-fitted unstructured grids. Any shape iteration requires re-meshing and 70+ hours of supercomputer convergence. StrataPhysics models the continuous function space directly.
By mapping between infinite-dimensional function spaces via spectral convolutions, our models are completely resolution-invariant: train on coarse wind-tunnel data, evaluate at sub-millimeter turbulent boundary layers with zero retraining.
Traditional neural networks blur supersonic shock fronts. StrataPhysics incorporates Godunov-invariant Riemann loss penalties that capture razor-sharp Mach 1 to Mach 10 oblique shock angles without numerical Gibbs oscillations.
Simultaneously solves aero-thermo-structural interactions: continuous aerodynamic pressure loads, high-enthalpy convective heat flux, and flexible wing flutter aeroelasticity coupled in a single unified tensor forward pass.
Multi-physics neural operators are among the most compute-intensive workloads in modern artificial intelligence. Pretraining across 1.2 Petabytes of hypersonic telemetry and evaluating 33 million continuous points per millisecond demands sustained high-throughput matrix acceleration.
Trained across 850,000 synthetic high-Mach flow simulations and hypersonic wind tunnel sensor traces.
Sustains 3.2+ TB/s per node memory throughput to prevent matrix starvation during 3D Fast Fourier Transforms (FFTs).
Custom-authored C++/CUDA kernels perform frequency-domain spectral cuts directly in hardware registers.
Founded by aerospace doctoral researchers and high-performance computing architects.
Ph.D. in Computational Fluid Dynamics & Aerospace Systems. Formerly principal aerothermodynamics researcher developing spectral boundary layer methods for high-Mach reentry flight. Lead author of foundational publications in Fourier Neural Operator convergence on non-smooth Riemannian manifolds.
Doctorate in Applied Mathematics. Specializes in non-linear hyperbolic conservation laws, Riemann solvers, and physics-informed continuous deep learning.
High-performance computing architect. Specialist in parallel multi-node 3D FFT distributions, low-latency ring AllReduce algorithms, and FP8 kernel tuning.
Complete with market size ($120B TAM), unit economics, technical benchmarks against supercomputers, and hardware allocation roadmaps.