employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics

Theoretical validation is usually based on attracting clear distinguishing and descriptive features in appropriately reduced experimental data, whereas AB-
The AB-de-novo simulation of interpreting experimental data often requires complete knowledge about initial conditions and parameters.
We apply machine learning methods here to overcome these natural limitations.
We outline some basic general ideas and show how to use them to solve long term problems
Standing in the difficulties of theory and experiment, in the problem of highLaser intensity-
Plasma Interaction
In particular, we show how the artificial neural network \"reads\" the features of the laser imprint
The plasma harmonic spectrum currently analyzed by spectral interference method.
In the past few years, the use of machine learning has opened up new prospects in many physical fields, including plasma physics, condensation-
Physical physics, quantum physics, thermodynamics, quantum chemistry, particle physics, and so on.
Recent examples include magnetic confinement fusion, inertial confinement fusion, discovery of phase transitions, closure of turbulence models, representation of quantum states, classification of galaxies, and application of orbital stability.
One of the origins of this progress is the possibility to process a large amount of data and draw conclusions based on features that do not allow direct description and evaluation in human language.
In this way, some natural limitations of human beings can be overcome, making machine learning a useful tool to produce fruitful synergies with traditional methods in theoretical and experimental physics.
One area of successful application of machine learning is related to model calibration problems.
The problem is based on incomplete and potentially inaccurate knowledge of the modeling system going down in a particular set of situations to find the appropriate parameters of the model.
Maximum likelihood estimation, Bayesian estimation, filtering and other statistical methods have been successfully applied in financial market analysis, hydrology, Urban Research, climatology and other fields.
However, the use of machine learning seems to be a promising option, which can offer some new opportunities.
In this article, we consider the opportunity to solve long-term problems using machine learning
Common problems in laserplasma physics.
We discussed the possibility of using difficult autonomous identificationto-
Verify and advance the features in the image-only model and the real or numerical experimental data that reconstruct the experimental conditions.
The only image model with laserplasma high-
Harmonic generation, we train an artificial neural network (NN)
Various parameters are reconstructed based on recognition of unspecified features in the harmonic spectrum.
Then, the neural network \"recognizes\" with AB-
We use it to simulate real experiments.
In this way, we can reconstruct the parameters of the experiment, or determine the most appropriate value for the free parameters of the incomplete theory.
This can also be used to determine the validity region of different models.
It is important that this method can be applied in the case of inaccurate or inherently incomplete understanding of the experimental conditions, I . E. e.
In the case of performing a specific AB
It is impossible to simulate from scratch.
In this way, this method can provide new approaches for experiments and provide new insights for theoretical and model development.
For completeness, let\'s take the Galton board as an example, starting with a discussion of basic ideas.
Then we provide a proof. of-
This method is used in the demonstration of the principle of the problem outlined in the laser field
Plasma Interaction