arXiv:2601.10885v2 Announce Type: replace-cross Abstract: We propose a methodology to infer collision operators from phase space data of plasma dynamics. Our approach combines a differentiable kinetic simulator, whose core component in this work is a differentiable Fokker-Planck solver, with a gradient-based optimisation method to learn the collisional operators that best describe the phase space dynamics. We test our method using data from two-dimensional Particle-in-Cell simulations of spatially uniform thermal plasmas, and learn the collision operator that captures the self-consistent elect
Advances in differentiable programming and kinetic simulation techniques are enabling refined parameter inference for complex physical systems, like plasma dynamics.
Accurate modeling of plasma behavior is crucial for progress in fusion energy, semiconductor manufacturing, and astrophysics, making efficient collision operator learning a significant enabler.
The ability to infer complex physical operators directly from phase space data via differentiable simulators allows for more precise and data-driven understanding of plasma dynamics.
- · Fusion energy research
- · Plasma physics researchers
- · AI/ML in scientific computing
- · Semiconductor manufacturing
- · Traditional empirical model development
- · Less data-driven plasma simulation approaches
More accurate and efficient plasma simulations accelerate research cycles in fusion and other plasma-dependent technologies.
Reduced need for extensive human-derived theoretical models as AI learns directly from observational data, potentially democratizing access to complex physics modeling.
Breakthroughs in fusion energy generation could alleviate global energy constraints, significantly impacting geopolitics and economic structures.
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