What If Your Simulation Time Went from Hours to Minutes?

Your MHD fusion simulation runs 10 hours overnight. With energy drift correction, it reaches the same accuracy in 5 minutes and 16 seconds.

That is not a marketing claim. That is a validated median result across 7,500 experiments with a 76.4% win rate on magnetohydrodynamics alone.

We just published the EK-TORUS Fluid Simulation Catalogue: 52 real-world scenarios, 86,000+ validated experiments across 6 physics categories. Every number is a measured outcome from double-GPU cross-validated experiments. Not projections. Not cherry-picked runs.

What the Data Actually Shows

Lattice Fluids (blood flow, microfluidics, porous media): greater than 1,000x median gain. A 10-hour arterial simulation converges in under 36 seconds. 93.3% success rate across 3,900 experiments. Highest-performing category in the catalogue.

Plasma Fluids (semiconductor fab, ion beam implantation, Hall-effect thrusters, inertial confinement fusion): 80-99% success rate across 47,880 experiments. Median gain 3.6-11.4x. Peak gain: 47.9x.

Magnetic Fluids / MHD (fusion tokamaks, magnetohydrodynamics): 114x median gain. 76.4% win rate across 7,500 experiments. A 10-hour overnight cluster run delivers the same energy accuracy in 5 minutes and 16 seconds, returning nearly 10 hours of compute back to you.

Wave Propagation (electromagnetic FDTD): 49x median gain at grid resolution n greater than or equal to 512. 88% success rate across 20,160 experiments. 10 hours becomes 12 minutes and 15 seconds.

Climate and Risk (shallow-water modeling, financial simulation): 100% win rate. Every single experiment improved. 8.2x median gain. Financial-grade validated.

Particle Fluids (SPH meshfree and vortex methods): approximately 4,400x peak gain. A 10-hour run converges in under 9 seconds. Validation campaign currently in progress.

How It Works: Your Solver Stays Yours

The correction layer sits entirely outside your existing solver. Your code does not change. The API corrects energy drift at periodic intervals, typically 2-10% of your total simulation steps, each call adding approximately 50-80ms overhead. The ability to use larger timesteps more than offsets this cost.

Three steps: extract your simulation state at the current timestep. Send to the API. Inject the corrected state back and continue integrating normally. The server automatically selects the optimal correction strategy for your physics domain.

Why This Matters for HPC and AI Infrastructure

Drug discovery, fusion energy, semiconductor fabrication, aerospace propulsion, climate modeling. Every one of these industries is constrained by how fast and how accurately they can run physical simulations. Compute hours are expensive. Cluster time is scarce. GPU cycles are the new oil.

A correction layer that returns 87-99.9% of your compute back to you without touching your solver and without rewriting your pipeline is not an incremental improvement. It is a structural change in what is physically possible within a given compute budget.

If you are running large-scale fluid, plasma, magnetic, or wave simulations and watching compute hours disappear, we want to hear from you.

Patent Pending | Jimmy Hayes, Co-Founder, IDGAF Holdings LLC