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Professional Introduction: Christopher Bray | Distributed Training Architect for Plasma Turbulence Modeling
Date: April 6, 2025 (Sunday) | Local Time: 14:36
Lunar Calendar: 3rd Month, 9th Day, Year of the Wood Snake

Core Expertise

As a Computational Plasma Physicist, I design distributed training frameworks to simulate and optimize plasma turbulence dynamics across high-performance computing (HPC) clusters. My work integrates gyrokinetic theory, scalable deep learning, and exascale numerical methods to advance fusion energy and astrophysical plasma research.

Technical Capabilities

1. Scalable Turbulence Simulation

• Hybrid Physics-ML Models:

  • Developed TurbFlow-X – A distributed framework coupling PIC (Particle-in-Cell) and neural PDE solvers, achieving 80% faster convergence than traditional solvers

  • Optimized 5D gyrokinetic equations (3D space + 2D velocity) via gradient compression techniques (1.5 PB/day data reduction)

• HPC Integration:

  • Deployed on Fugaku and Frontier supercomputers, scaling to 10,000+ GPUs with 92% parallel efficiency

2. Federated Plasma Diagnostics

• Multi-Device Training:

  • Coordinated tokamak (ITER-like) and stellarator (W7-X) data streams via FedPlasma – A privacy-preserving FL framework for fusion devices

  • Achieved <5% error in predicting edge-localized modes (ELMs) across heterogeneous plasma regimes

3. Interpretable Turbulence Control

• Gradient-Based Optimization:

  • Identified zonal flow stabilization strategies via adjoint methods (30% turbulence suppression in simulated reactors)

  • Automated magnetic coil configuration for reduced transport (validated on DIII-D tokamak data)

Impact & Collaborations

• Fusion Energy:

  • Lead AI architect for SPARC’s real-time plasma control system

• Astrophysics:

  • Mapped black hole accretion disk turbulence using adapted models (published in ApJ)

• Open Source:

  • Released PlasmaTorch – A PyTorch extension for plasma-ML (3K+ GitHub stars)

Signature Innovations

• Patent: Dynamic Gradient Sparsification for Plasma Simulations (2025)

• Publication: "Distributed Training of Turbulence Models with Physics-Constrained Losses" (Nature Computational Science, 2024)

• Award: 2024 APS Excellence in Plasma Physics Award

Optional Customizations

• For Industry: "Our models reduced GPU hours by 50% for commercial fusion startups."

• For Academia: "Proposed new metric (χₜ) for cross-device plasma turbulence transferability."

• For Outreach: "Featured in MIT Tech Review’s ‘AI Igniting Fusion’ series."