Deep learning for thermal plasma simulation: solving 1-D arc model as an example

DOI10.1016/j.cpc.2020.107496zbMath1511.82041arXiv2003.07550OpenAlexW3010832613MaRDI QIDQ6097959

Bingyu Wu, Linlin Zhong, Qi Gu

Publication date: 7 June 2023

Published in: Computer Physics Communications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.07550

zbMATH Keywords

partial differential equationsdeep learningdeep neural networksarc plasma modelingthermal plasma simulation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Statistical mechanics of plasmas (82D10) Ionized gas flow in electromagnetic fields; plasmic flow (76X05)

Related Items (2)

DeepBHCP: deep neural network algorithm for solving backward heat conduction problems ⋮ NAS-PINN: neural architecture search-guided physics-informed neural network for solving PDEs

Cites Work

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• Model-based parameter optimization for arc welding process simulation
• Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
• DGM: a deep learning algorithm for solving partial differential equations
• A desktop computer model of the arc, weld pool and workpiece in metal inert gas welding
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Solving high-dimensional partial differential equations using deep learning
• fPINNs: Fractional Physics-Informed Neural Networks

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