arXiv (All)
open access
Machine Learning Accelerated Plasma Simulation through Physics Guided Time Jumps
We present a machine-learning-accelerated plasma simulation scheme that combines learned time jumps with ordinary physics evolution. A neural network corrects a low-cost physics forecast over multiple time steps, while the original solver reconstructs the state and advances it between jumps through
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
- Vidyut3d: A GPU accelerated fluid solver for non-equilibrium plasmas on adaptive grids#495548
- Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology#986298
- SNAP-FM: Sparse Nonlinear Accelerated Projection for Physics-Constrained Generative Modeling#926102
- Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks#290579
- Physics-Informed Neural Network Surrogates with Polynomial Chaos-Based Uncertainty Propagation for Stochastic Model Predictive Control#1048826
Record · ID 1049100
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