StrataPhysics
Foundation Neural PDE Solvers for Hypersonic Aerospace & Multi-Physics
Delivering 19,400x supercomputing acceleration over classical Navier-Stokes CFD meshes. Enabling real-time aeroelastic flutter prediction, oblique shockwave coupling, and hypersonic non-equilibrium aerothermodynamics.
Classical CFD Meshing Stalls Advanced Aerospace Innovation
- Grid Generation Latency: Generating body-fitted 100M-cell meshes takes 2 to 4 weeks of specialized engineering labor per geometry variant.
- Extreme Compute Burn: Solving non-linear Navier-Stokes equations consumes 72+ wall-clock hours across expensive supercomputer clusters.
- Supersonic Discontinuities: Standard numerical schemes produce catastrophic non-physical Gibbs oscillations at hypersonic shock boundaries.
Aerospace defense OEMs, commercial supersonic aircraft designers, and satellite reentry programs spend billions on iterative wind tunnel tests because current simulation tools are too slow for real-time design optimization.
The StrataPhysics Multi-Physics Foundation Engine
We train continuous-time Fourier Neural Operators across infinite-dimensional Banach function spaces, eliminating 3D mesh generation and delivering millisecond-latency aerodynamic solutions.
Fourier Neural Operators on Riemannian Manifolds
1. Spectral Convolution Kernels
Computes non-local physical interactions directly in the frequency domain via parameterized 3D Fast Fourier Transforms.
2. Godunov-Invariant Shock Capturing
Enforces Rankine-Hugoniot jump conditions inside neural latent layers, resolving razor-sharp oblique shock waves without blur.
3. Warp-Level Register Matrix Fusion
Eliminates Host-to-Device memory bottlenecks by performing all spectral cuts directly within high-throughput accelerator registers.
Where \(R_\theta\) parameterizes the learned Fourier multiplier matrix, transforming continuous aerospace geometry fields directly into 3D velocity and pressure vectors.
End-to-End Scientific Supercomputing Stack
Mesh-free STEP / IGES point cloud projection in under 0.8 seconds.
Multi-physics foundation operator pretrained across 1.2 Petabytes of CFD & flight telemetry.
Sub-microsecond direct C++/CUDA memory mapped tensor export to in-flight computers.
Distributed parallel evaluation across 256 matrix accelerator nodes.
Orders of Magnitude Superiority Over Legacy Solvers
| SIMULATION TASK | STRATAPHYSICS NEURAL PDE | LEGACY SUPERCOMPUTER CFD (OPENFOAM/FLUENT) | GENERIC DEEP LEARNING (PINNS) |
|---|---|---|---|
| Transonic Wing (Mach 0.85) | 12.4 Milliseconds | 54.2 Hours | 4.5 Hours (Poor generalization) |
| Hypersonic Waverider (Mach 7.2) | 21.2 Milliseconds | 148.0 Hours | Failed (Shock divergence) |
| Grid Generation Labor | 0 Seconds (Mesh-Free) | 2 to 3 Weeks Manual Work | Requires Point Sampling |
| Parallel Evaluations / Sec | 65,536 Continuous Queries | < 0.0001 (Sequential) | 12 Queries |
Capturing the $120B High-Performance Simulation Wave
Global aerospace computational engineering, defense simulation, automotive aerodynamics, and climate forecasting by 2030.
Tier-1 aerospace defense contractors, commercial rocket propulsion teams, and turbine manufacturers.
Next-generation hypersonic flight and defense propulsion startups seeking real-time aerothermal trajectory solvers.
Dual Platform Licensing & Supercomputing Compute
Hardware Grant Compute Justification
Our foundation neural PDE models require dedicated high-density matrix arithmetic across distributed clusters:
Custom warp-synchronous reduction routines running in dense accelerator registers, tripling FFT throughput.
Sustained 3.2+ TB/s per node memory throughput eliminates matrix starvation across 33M continuous points.
Pretraining our 70B multi-physics model across 256-node matrix clusters with 3.2 Tbps optical interconnects.
Requesting 72,000 accelerated node hours to scale commercial hypersonic flight optimization and aerothermodynamic validation.
Active Defense & Commercial Aerospace Pilots
Hypersonic Reentry Program
Real-time aeroelastic heat flux prediction at Mach 6.8 with 99.8% temperature field accuracy.
Commercial Aircraft OEM
Automated wing supercritical shock buffet suppression, compressing design cycle from 9 months to 3 weeks.
Gas Turbine Aerodynamics
High-temperature combustion and blade turbulence simulation evaluating 50,000 cooling variants in 4 hours.
Doctoral & Supercomputing Leadership
Vikramaditya Roy
Ph.D. in Computational Fluid Dynamics. Pioneer of Fourier Neural Operators for high-Mach aerothermodynamics and continuous shock capturing.
Dr. Anya Kasparova
Doctorate in Applied Mathematics. Specializes in hyperbolic conservation laws, Riemann invariants, and physics-informed continuous deep learning.
Tariq Lin
High-performance computing architect. Specialist in parallel multi-node 3D FFT distributions, low-latency ring AllReduce algorithms, and FP8 tuning.
Raising $5.0M Seed + Supercomputing Cluster Credits
- 50% Cluster Pretraining & Hardware: Scale continuous 70B multi-physics models across dedicated accelerator nodes.
- 35% Palo Alto Research Team: Expand doctoral applied mathematics, spectral solver, and kernel engineering talent.
- 15% Commercial Pilot Expansion: Deliver production integrations with 6 Tier-1 aerospace and defense contractors.
Direct Founder Contact
Connect directly with Vikramaditya Roy (CEO) for cap table review, compute incubator discussions, or technical data room access.