PLASMION AI
MATHEMATICAL FOUNDATIONS & COMPUTE PIPELINE

Solving Continuous Magnetohydrodynamics at Sub-Microsecond Latency

How Plasmion AI converts multi-scale partial differential equations into real-time tensor inference, achieving 200 kHz closed-loop magnetic stability across thermonuclear plasma reactors.

THEORETICAL FRAMEWORK

Infinite-Dimensional Fourier Neural Operators (FNO)

Standard convolutional or transformer architectures struggle with continuous physical systems because they discretize space on rigid grids. When magnetic flux lines twist during tearing modes, grid artifacts trigger fatal numerical errors.

Plasmion-1 parameterizes the integral kernel directly in the continuous Fourier domain on a toroidal Riemannian manifold $\mathcal{M} = \mathbb{T}^2 \times \mathbb{R}^+$. By computing spectral convolutions in Sobolev spaces $\mathcal{H}^s(\Omega)$, our model learns operators invariant to spatial resolution.

// Governing Continuous Spectral Operator Update:
v_{t+1}(x) = \sigma \Big( W v_t(x) + \mathcal{F}^{-1} \big( R_{\phi} \cdot (\mathcal{F} v_t) \big)(x) + \mathcal{P}_{\text{MHD}}(v_t)(x) \Big)
Where $\mathcal{P}_{\text{MHD}}$ guarantees divergence-free magnetic induction: $\nabla \cdot \mathbf{B} = 0$.

Key Invariance Guarantees

Divergence-Free Field Constraint Loss penalized by $\|\nabla \cdot \mathbf{B}\|_{\mathcal{L}^2} \le 10^{-7}$, preventing unphysical magnetic monopoles.
Magnetic Helicity Conservation Invariant $K = \int (\mathbf{A} \cdot \mathbf{B}) \, dV$ maintained under finite Lundquist numbers $S \ge 10^8$.
Zero-Shot Aspect Ratio Transfer Generalizes seamlessly between aspect ratios $A = 1.35$ (Spherical) and $A = 3.1$ (Conventional Tokamaks).
LATENCY BUDGET BREAKDOWN TOTAL: 4.8 µs
Direct DMA Sensor Ingestion: 0.8 µs
Tensor Matrix Kernel Evaluation: 3.2 µs
Optic Coil Power Inverter Trigger: 0.8 µs

Deterministic jitter < 42 nanoseconds verified across 10,000 continuous test pulses.

HARDWARE-IN-THE-LOOP INTEGRATION

Deterministic Microsecond Hardware Stack

Controlling a burning plasma cannot tolerate operating system latency, kernel interrupts, or network packet jitter. Plasmion deploys a bare-metal execution environment.

Our runtime utilizes direct-to-memory PCIe DMA pipelines from multi-channel magnetic pick-up coils and ECE radiometry systems directly into high-throughput tensor accelerator matrix units. Power converter gating vectors are dispatched over deterministic optical links in sub-microsecond cycles.

PETASCALE COMPUTE DEMAND

Massive Synthetic Physics Generation & Pretraining

Real tokamaks only pulse a few times per day, providing scarce experimental anomaly data. Plasmion overcomes data scarcity by training on over 100 million synthetically generated 3D MHD discharges computed across petascale distributed accelerator clusters.

18.4 Billion
Parameter Model Capacity

Unifying kinetic electron dynamics with macroscopic fluid motion.

42 Petabytes
Synthetic Turbulence Dataset

Multi-scale phase-space data synthesized with conservation laws.

99.8%
Disruption Mitigation Rate

Zero catastrophic thermal quenches in high-density beta regimes.