Research Paper#Plasma Physics, Uncertainty Quantification, Neural Networks🔬 ResearchAnalyzed: Jan 3, 2026 15:46
Tensor Neural Surrogates for Plasma Uncertainty Quantification
Published:Dec 30, 2025 13:07
•1 min read
•ArXiv
Analysis
This paper addresses the computationally expensive problem of uncertainty quantification (UQ) in plasma simulations, particularly focusing on the Vlasov-Poisson-Landau (VPL) system. The authors propose a novel approach using variance-reduced Monte Carlo methods coupled with tensor neural network surrogates to replace costly Landau collision term evaluations. This is significant because it tackles the challenges of high-dimensional phase space, multiscale stiffness, and the computational cost associated with UQ in complex physical systems. The use of physics-informed neural networks and asymptotic-preserving designs further enhances the accuracy and efficiency of the method.
Key Takeaways
- •Proposes a variance-reduced Monte Carlo framework for uncertainty quantification in the Vlasov-Poisson-Landau (VPL) system.
- •Employs tensor neural network surrogates to replace costly Landau collision term evaluations.
- •Utilizes physics-informed neural networks and asymptotic-preserving designs to improve accuracy and efficiency.
- •Demonstrates substantial variance reduction, accurate statistics, and lower wall-clock time in numerical experiments.
Reference
“The method couples a high-fidelity, asymptotic-preserving VPL solver with inexpensive, strongly correlated surrogates based on the Vlasov--Poisson--Fokker--Planck (VPFP) and Euler--Poisson (EP) equations.”