Simplifying plasma chemistry via ILDM

Rehman, T Tafizur; Ke­man­eci, Efe; Graef, Waad Wouter; Dijk, van J Jan

doi:10.1088/1742-6596/682/1/012035

Cited by 8 publications

(11 citation statements)

References 13 publications

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“…Moreover, a significant speedup in central processing unit (CPU) time has been obtained due to the fact that PCA reduced not only the number of variables but also the stiffness of the equations. The ILDM method has been applied by Rehman et al 81 to Ar and H 2 plasma kinetic schemes. It has been shown that the ILDM method automatically extracts the relevant information about fast and slow time scales in a chemical reaction system.…”

Section: Derivation Of Reduced Reaction Mechanismsmentioning

confidence: 99%

“…After a short interval of time, the fast time scale processes will quickly move toward this low-dimensional manifold and the slow time scale processes will move tangential or along the manifold to finally reach the equilibrium point. 81 Despite successful application in plasma chemical systems, PCA and ILDM also present some drawbacks. For example, in PCA, the manifolds are constructed based on the hypotheses of linearity between original and transformed variables that cannot be always satisfied for complex chemical networks.…”

Section: Derivation Of Reduced Reaction Mechanismsmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

“…It has been shown that the ILDM method automatically extracts the relevant information about fast and slow time scales in a chemical reaction system. After a short interval of time, the fast time scale processes will quickly move toward this low-dimensional manifold and the slow time scale processes will move tangential or along the manifold to finally reach the equilibrium point 81 . Despite successful application in plasma chemical systems, PCA and ILDM also present some drawbacks.…”

Section: Reviewmentioning

confidence: 99%

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Review: Machine learning for advancing low-temperature plasma modeling and simulation

Trieschmann,

Vialetto,

Gergs

2023

J. Micro/Nanopattern. Mats. Metro.

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Machine learning has had an enormous impact in many scientific disciplines. It has also attracted significant interest in the field of low-temperature plasma (LTP) modeling and simulation in past years. Its application should be carefully assessed in general, but many aspects of plasma modeling and simulation have benefited substantially from recent developments within the field of machine learning and datadriven modeling. In this survey, we approach two main objectives: (a) we review the state-of-the-art, focusing on approaches to LTP modeling and simulation. By dividing our survey into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, we aim to extensively discuss relevant examples from literature. (b) We provide a perspective of potential advances to plasma science and technology. We specifically elaborate on advances possibly enabled by adaptation from other scientific disciplines. We argue that not only the known unknowns but also unknown unknowns may be discovered due to the inherent propensity of data-driven methods to spotlight hidden patterns in data.

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Section: Derivation Of Reduced Reaction Mechanismsmentioning

confidence: 99%

Section: Derivation Of Reduced Reaction Mechanismsmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Trieschmann,

Vialetto,

Gergs

2023

J. Micro/Nanopattern. Mats. Metro.

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“…Applications of CSP or ILDM to plasma chemistry are still scarce, an example can be found in [656]. The reason may be a lack of awareness within the community of such reduction methods, or the fact that it simply takes more time for techniques to transfer to a different field of science.…”

Section: H Reduction Of Chemical Reaction Modelsmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Extension of the Reaction-Diffusion Manifold method to systems with ionization

Straßacker

Maas

2022

Combustion and Flame

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Simplifying plasma chemistry via ILDM

Cited by 8 publications

References 13 publications

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Review: Machine learning for advancing low-temperature plasma modeling and simulation

2022 Review of Data-Driven Plasma Science

Extension of the Reaction-Diffusion Manifold method to systems with ionization

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