arXiv CS
open access
NEOPIC: A Neural Operator Framework for Particle-based Kinetic Plasma Simulations
We introduce NEOPIC, a framework for learning the field operator in particle-based methods applied to kinetic plasma simulations. Its key novelty is a neural operator that acts directly on the empirical measure of the particles, rather than on a grid, and is evaluated at the particle positions. No m
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- NEOPIC: A Neural Operator Framework for Particle-based Kinetic Plasma Simulations#1492136
- PTNO: Training Neural Operators with Noisy Monte Carlo Estimates for Particle Transport Problems#1154359
- PTNO: Training Neural Operators with Noisy Monte Carlo Estimates for Particle Transport Problems#1136839
- Fast-forward prediction of lattice Boltzmann dynamics with physics-informed neural operators#1459776
- Physics-Informed Neural Plasticity: PDE Solvers That Reshape Themselves#1233549
Record · ID 1482993
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive