惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Jina AI
Jina AI
博客园 - 【当耐特】
量子位
C
Check Point Blog
博客园 - 叶小钗
博客园 - 聂微东
博客园 - 三生石上(FineUI控件)
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Hugging Face - Blog
Hugging Face - Blog
美团技术团队
The Cloudflare Blog
T
Tailwind CSS Blog
人人都是产品经理
人人都是产品经理
月光博客
月光博客
V
V2EX
Last Week in AI
Last Week in AI
酷 壳 – CoolShell
酷 壳 – CoolShell
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
IT之家
IT之家
大猫的无限游戏
大猫的无限游戏
有赞技术团队
有赞技术团队
Apple Machine Learning Research
Apple Machine Learning Research
S
SegmentFault 最新的问题
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
An Energy-Conserving Unstaggered Electromagnetic-Potentia...
[Submitted on 13 Jun 2026] · 2026-06-16 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:We develop an unstaggered, potential-based particle-in-cell method for the nonrelativistic Vlasov-Maxwell system in the Lorenz gauge. The field update is written as a Crank-Nicolson discretization of first-order wave systems for the scalar potential, the vector potential, and their time derivatives. The charge density is not deposited directly; instead, it is advanced from the discrete continuity equation using the current deposited from the particles. This opens up algorithmic flexibility with a range of innovation, including unstaggered mesh layouts that preserve the Lorenz gauge and Gauss's law at the discrete level. In the potential formulation, this source ordering also permits preservation of the Lorenz gauge and Gauss's law at the discrete level. To extend the paradigm to an energy-conserving formulation, we introduce a consistent orbit-averaged scatter, gather, and particle push. For energy consistency, the update of the canonical momentum is modified by replacing the pointwise midpoint derivative of the vector potential with an orbit-averaged discrete gradient of the mesh-interpolated vector potential consistent with the orbit-average maps. This construction satisfies an exact finite-difference chain rule along each particle orbit. As a result, the particle work equals the mesh work appearing in the Crank-Nicolson field-energy balance, yielding exact total-energy conservation up to nonlinear solver tolerance and roundoff. We demonstrate exact energy conservation of the method in 3D on the cold two-stream instability.

Submission history

From: Sining Gong [view email]
[v1] Sat, 13 Jun 2026 00:33:53 UTC (349 KB)