Fusion & Nuclear

Energy / Fusion Control

Deep reinforcement learning directly commands tokamak magnetic coils to hold diverse plasma configurations, a landmark public case of AI controlling a real physical device.

Electromagnetics

A GPU-accelerated implicit solver for electromagnetic field computations in full 3D tokamak simulations, featuring co-designed hierarchical domain decomposition and fast exact local solver for efficient large-scale parallel GPU computing.

Electromagnetics

The Anisotropic Maxwell Neural Operator enables rapid parametric modeling of ion cyclotron resonance heating full-wave responses for the EAST tokamak by learning the one-parameter dielectric-field family within a single operator.

Plasma / Fusion

Presents PQLS, a quasilinear gyrokinetic transport solver using an eigenvalue solver in general magnetic geometry with Bayesian saturation-rule closure to improve turbulent transport prediction in tokamak plasmas.

CFD

Proposes a neural operator-based surrogate model for CFD-level transient thermal-hydraulic simulation of helical coil steam generators in SMRs, enabling real-time digital twin applications.

Electromagnetics

Proposes a KAN-based single-parameterized surrogate model to accelerate drift kinetic equation solving for fast neoclassical toroidal viscosity torque calculation in tokamak plasma modeling.

Thermal Management

Proposes a validation-gated multi-agent governance framework enabling thermal-hydraulic surrogate models to adapt online during operating-regime shifts, preventing condition-locking issues in offline-pretrained models.

Electromagnetics

A physics-informed neural operator learns the map between function spaces in magnetohydrodynamic simulations, enabling efficient state inference for magnetic reconnection at any parameter and time step, significantly reducing computational cost at high Lundquist numbers.