Plasmion AI trains Continuous Neural Operator world models to predict, suppress, and eliminate plasma micro-instabilities at 200 kHz. Enabling stable burning plasma (Q > 10) and unlocking multi-gigawatt baseload clean power.
Magnetic confinement reactors generate plasmas hotter than the core of the Sun. At high pressure, nonlinear magnetohydrodynamic instabilities trigger abrupt thermal quenches in milliseconds—damaging multi-billion dollar vacuum vessels.
Turbulent eddies on the ion gyroradius scale leak heat faster than alpha particles can sustain self-heating, quenching the thermonuclear burn before net-energy breakeven is reached.
Neoclassical Tearing Modes (NTMs) and Edge Localized Modes (ELMs) collapse magnetic flux surfaces within 5 milliseconds, depositing gigajoules of thermal energy onto plasma-facing divertor armor.
First-principles ab-initio simulations take 3 weeks on high-performance supercomputing clusters to model just 50 microseconds of plasma time. Real-time feedback control cannot wait for HPC clusters.
Our physics-informed neural foundation models bridge ab-initio mathematical physics and microsecond hardware control.
An 18-billion parameter Fourier Neural Operator trained across millions of simulated gyrokinetic discharges and multi-tokamak experimental diagnostics. Resolves continuous 3D magnetic flux geometries with zero spatial discretization limits.
Real-time deep reinforcement learning policy executing at 200 kHz directly inside the reactor's FPGA/Tensor matrix cluster. Modulates currents across 128 high-temperature superconducting (HTS) poloidal field coils.
Autonomous anomaly detector providing a 150-millisecond advance warning window prior to density limit disruptions. Automatically engages shattered pellet injection (SPI) and resonant magnetic perturbations (RMP) if instability exceeds safe thresholds.
Quantitative benchmarks comparing Plasmion-1 against industry-standard numerical solvers and classical PID controllers.
| METRIC / CAPABILITY | PLASMION-1 FOUNDATION ENGINE | JOREK 3D MHD SOLVER | NIMROD EXTENDED MHD | CLASSICAL PID CONTROLLER |
|---|---|---|---|---|
| Closed-Loop Control Rate | 200 kHz (5 µs loop) | Offline only (hrs/step) | Offline only (days/step) | 10 kHz (100 µs loop) |
| Disruption Warning Window | 150 ms advance notice | Non-predictive | Non-predictive | < 15 ms (Reactive) |
| Nonlinear Plasma Dynamics | Fully Modeled (Sobolev PDEs) | Accurate but compute-bound | Accurate but compute-bound | Linearized Only (Diverges) |
| Multi-Tokamak Portability | Zero-Shot Adaptable Geometry | Manual re-meshing (months) | Manual re-meshing (months) | Device-specific hand tuning |
| Thermal Quench Prevention Rate | 99.8% Successful Mitigation | N/A (Simulation Only) | N/A (Simulation Only) | 62.4% (Frequent Divertor Scrape) |
Examine our addressable market in clean commercial fusion ($84B+), hardware tensor compute scaling requirements, multi-device empirical validation, and 24-month roadmap.
Bridging elite computational plasma research and extreme-scale parallel tensor engineering.
Founder & Chief Executive Officer
Ph.D. in Computational Plasma Physics & Applied Mathematics (MIT / Princeton Plasma Physics Laboratory). Author of 14 seminal papers on Neural Operators for Magnetohydrodynamics.
Chief Science Officer & Co-Founder
Ph.D. Imperial College London. 12+ years in tokamak MHD theory, non-inductive current drive, and resonant magnetic perturbation stability modeling.
VP of AI Systems & HPC
M.S. Stanford University. Specialist in low-latency petascale distributed tensor acceleration, kernel-level FP8 matrix optimizations, and sub-microsecond FPGA pipelines.
Head of Commercial & Strategy
MBA Harvard Business School, B.S. Nuclear Engineering. Former director of strategic partnerships in advanced energy infrastructure and public-private utility ventures.
Plasmion-1 decouples continuous physics prediction from actuator inversion. The foundation model continuously predicts the 3D boundary flux state 150 ms forward, while our ultra-compact distilled policy runs on high-speed tensor accelerators directly interfaced to the power supplies via deterministic PCIe interconnects, achieving total latency under 5 microseconds.
Yes. By employing mesh-free Fourier Neural Operators defined on continuous Riemannian manifolds, the model represents magnetic equilibria coordinate-independently. We have validated zero-shot and few-shot transfer between compact spherical designs and conventional aspect ratio tokamaks.
Training involves massive distributed tensor accelerator clusters with high-bandwidth memory interconnects, processing petabytes of synthetic 6D gyrokinetic kinetic turbulence simulations and historical Thomson scattering, ECE, and magnetics telemetry.
Whether you operate an experimental magnetic confinement facility, develop advanced tokamak reactors, or seek co-development partnerships, Plasmion AI is your physics-grade intelligence layer.