2022 review of data-driven plasma science
Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …
review article highlights the latest development and progress in the interdisciplinary field of …
A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network
High-resolution flow field reconstruction is prevalently recognized as a difficult task in the
field of experimental fluid mechanics, since the measured data are usually sparse and …
field of experimental fluid mechanics, since the measured data are usually sparse and …
Drift reduced Landau fluid model for magnetized plasma turbulence simulations in BOUT++ framework
Recently the drift-reduced Landau fluid six-field turbulence model within the BOUT++
framework [1] has been upgraded. In particular, this new model employs a new …
framework [1] has been upgraded. In particular, this new model employs a new …
Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
Constitutive models are widely used for modeling complex systems in science and
engineering, where first-principle-based, well-resolved simulations are often prohibitively …
engineering, where first-principle-based, well-resolved simulations are often prohibitively …
Machine learning moment closure models for the radiative transfer equation I: directly learning a gradient based closure
In this paper, we take a data-driven approach and apply machine learning to the moment
closure problem for the radiative transfer equation in slab geometry. Instead of learning the …
closure problem for the radiative transfer equation in slab geometry. Instead of learning the …
Neural-network based collision operators for the Boltzmann equation
Kinetic gas dynamics in rarefied and moderate-density regimes have complex behavior
associated with collisional processes. These processes are generally defined by …
associated with collisional processes. These processes are generally defined by …
Machine learning moment closure models for the radiative transfer equation II: Enforcing global hyperbolicity in gradient-based closures
This is the second paper in a series in which we develop machine learning (ML) moment
closure models for the radiative transfer equation (RTE). In our previous work [J. Huang, Y …
closure models for the radiative transfer equation (RTE). In our previous work [J. Huang, Y …
Differentiable physics: A position piece
B Ramsundar, D Krishnamurthy… - arXiv preprint arXiv …, 2021 - arxiv.org
Differentiable physics provides a new approach for modeling and understanding the
physical systems by pairing the new technology of differentiable programming with classical …
physical systems by pairing the new technology of differentiable programming with classical …
Data-driven, multi-moment fluid modeling of Landau damping
Deriving governing equations of complex physical systems based on first principles can be
quite challenging when there are certain unknown terms and hidden physical mechanisms …
quite challenging when there are certain unknown terms and hidden physical mechanisms …
Unveiling the interaction mechanisms of cold atmospheric plasma and amino acids by machine learning
ZN Chai, XC Wang, M Yusupov… - Plasma Processes and …, 2024 - Wiley Online Library
Plasma medicine has attracted tremendous interest in a variety of medical conditions,
ranging from wound healing to antimicrobial applications, even in cancer treatment, through …
ranging from wound healing to antimicrobial applications, even in cancer treatment, through …