[HTML][HTML] Deep-learning of parametric partial differential equations from sparse and noisy data
Data-driven methods have recently made great progress in the discovery of partial
differential equations (PDEs) from spatial-temporal data. However, several challenges …
differential equations (PDEs) from spatial-temporal data. However, several challenges …
Informed machine learning methods for application in engineering: A review
CT Mackay, D Nowell - Proceedings of the Institution of …, 2023 - journals.sagepub.com
Machine Learning (ML) has proved to be successful at identifying and representing
underlying relationships in large data sets which would be difficult to process manually …
underlying relationships in large data sets which would be difficult to process manually …
Physics-informed deep neural network for inhomogeneous magnetized plasma parameter inversion
Plasma parameter inversion is important for space plasma physics and applications,
particularly for inhomogeneous magnetized plasmas. A physics-informed deep neural …
particularly for inhomogeneous magnetized plasmas. A physics-informed deep neural …
Functional-hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions
Mathematical models used for the representation of (bio)-chemical processes can be
grouped into two broad paradigms: white-box or mechanistic models, completely based on …
grouped into two broad paradigms: white-box or mechanistic models, completely based on …
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 …
Parsimony-enhanced sparse Bayesian learning for robust discovery of partial differential equations
Robust physics discovery is of great interest for many scientific and engineering fields.
Inspired by the principle that a representative model is the simplest one among all possible …
Inspired by the principle that a representative model is the simplest one among all possible …
Data-driven modeling of Landau damping by physics-informed neural networks
Kinetic approaches are generally accurate in dealing with microscale plasma physics
problems but are computationally expensive for large-scale or multiscale systems. One of …
problems but are computationally expensive for large-scale or multiscale systems. One of …
Neural network model for parameter inversion in electromagnetic wave and plasma interaction systems
A multiple regression and machine learning approach is proposed to solve multiple plasma
parameter inversion in complex systems. For an electromagnetic (EM) wave and plasma …
parameter inversion in complex systems. For an electromagnetic (EM) wave and plasma …
Exploration of data-driven methods for multiphysics electromagnetic partial differential equations
In a complex electromagnetic environment, numerical solution of partial differential
equations (PDEs) and how to sample less data to invert spatio-temporal dynamics to …
equations (PDEs) and how to sample less data to invert spatio-temporal dynamics to …
Discovering hidden physical mechanisms in Bose–Einstein condensates via deep-learning
Discovering hidden physical mechanisms of a system, such as underlying partial differential
equations (PDEs), is an intriguing subject that has not yet been fully explored. In particular …
equations (PDEs), is an intriguing subject that has not yet been fully explored. In particular …