Rapid modeling method of chirped pulse amplification system and computer readable storage medium

Abstract

The invention discloses a rapid modeling method for a chirped pulse amplification system. The rapid modeling method comprises the following steps: acquiring multiple groups of CPA sample data with different parameters and a group of reference data with fixed parameters; respectively converting the sample data and the reference data from a time domain into a frequency domain, and respectively extracting the sample data and the reference data to obtain the frequency domain amplitude and the frequency domain phase of the pulse and the frequency domain amplitude and the frequency domain phase of the reference data; performing unwrapping operation on the phase of the sample data and the phase of the reference data, and extracting the phase with concentrated energy to obtain a difference phase with a small range; performing normalization operation on the frequency domain amplitude and the difference phase of the sample data, and inputting I (omega) obtained by splicing the frequency domain amplitude and the difference phase into a neural network for training so as to obtain predicted frequency domain amplitude and difference phase; and adding the difference phase and the reference data phase to obtain an absolute phase, obtaining a frequency domain envelope through the frequency domain amplitude and the absolute phase, and performing inverse Fourier transform to obtain the whole output of chirped pulse amplification.

Description

Technical Field

[0001] The present invention relates to the field of laser technology, and in particular to a fast modeling method for a chirped pulse amplification system capable of realizing fast disassembly. In addition, the present invention also relates to a computer-readable storage medium. Background Art

[0002] Chirped pulse amplification technology won the Nobel Prize in Physics in 2018. Chirped pulse amplification technology has promoted the development of high-power lasers, ultrafast lasers and nonlinear optics, and has been widely used in biomedicine, industrial precision machining, national defense security and basic science. Chirped pulse amplification technology can be divided into three processes: broadening, amplification and compression. First, a short laser pulse to be amplified is broadened in the time domain by introducing dispersion. The broadened pulse will be affected by nonlinear effects during the gain fiber amplification process, resulting in complex pulse phase changes. When compressed at the end, it is sensitive to pulse phase errors, otherwise the pulse will be distorted and cannot be compressed. Therefore, it is necessary to accurately capture the phase changes to achieve accurate modeling of chirped pulse amplification technology.

[0003] However, current methods for capturing phase changes in chirped pulse amplification simulations have various drawbacks, such as:

[0004] Method 1: Using the idea of ​​step-by-step and iteration, the split-step Fourier algorithm (SSFM) and the fourth-order Runge-Kutta algorithm (RK4) are used to numerically solve the chirped pulse amplification process by solving the generalized nonlinear Schrödinger equation (GNLSE) and the rate equation (RE) respectively, thereby capturing the phase change in the chirped pulse amplification process. This method is slow and has high complexity.

[0005] Method 2: Adopting the idea of ​​data-driven, use method 1 to generate training data, train the neural network, and then make predictions through the neural network. Since the phase value of the unwrapped pulse covers a large range, and the pulse compression process is sensitive to phase errors, the phase prediction accuracy of the neural network is difficult to meet the requirements. For example, for a pulse with a pulse width of 120fs, the phase range after phase unwrapping during the chirped pulse amplification process is 0 to 100,000 rad, and the phase error that meets the pulse compression requirements needs to be less than 0.01 rad, which is difficult to achieve in the current neural network model.

[0006] In view of this, it is necessary to design a fast modeling method for chirped pulse amplification system to solve the above problems. Summary of the invention

[0007] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0008] To this end, the present invention provides a fast modeling method for a chirped pulse amplification system, which can achieve high-precision prediction of pulse phase changes during the chirped pulse amplification process.

[0009] According to a first aspect of the present invention, a rapid modeling method for a chirped pulse amplification system is provided, comprising:

[0010] S1. Obtain multiple sets of CPA sample data E(t) with different parameters and a set of benchmark data with fixed parameters

[0011] S2, sample data E(t) and benchmark data Convert from time domain to frequency domain E(ω), The transformed frequency domain is extracted to obtain the frequency domain amplitude A(ω) and frequency domain phase φ(ω) of the pulse, as well as the frequency domain amplitude of the reference data. and frequency domain phase

[0012] S3. Compare the phase φ(ω) of the sample data with the phase of the reference data Perform an unwrapping operation and extract the phase where the energy is concentrated to obtain a difference phase Δ(ω) with a small range;

[0013] S4, respectively normalizing the frequency domain amplitude A(ω) and the difference phase Δ(ω) of the sample data, and inputting the concatenated I(ω) into the neural network for training to obtain the predicted frequency domain amplitude A(ω) and difference phase Δ(ω);

[0014] S5, compare the difference phase Δ(ω) with the reference data phase The absolute phase φ(ω) is obtained by adding them together, and the frequency domain envelope E(ω) is obtained by the frequency domain amplitude A(ω) and the absolute phase φ(ω). Then an inverse Fourier transform (ifft) is performed to obtain the entire output of the chirped pulse amplification.

[0015] Beneficial effects of the present invention: The rapid modeling method of the chirped pulse amplification system improves the phase prediction accuracy of the neural network by subtracting the frequency domain phase of the sample data from the reference frequency domain phase before training the neural network, because the phase change in the chirped pulse amplification process requires accurate prediction, and the phase change of the chirped pulse is complex, and the range after unwinding is large. Therefore, compared with the currently commonly used SSFM+RK4 method 1, the method of the present application is fast and has low complexity in modeling the chirped pulse amplification system; and compared with the direct data-driven method 2, the method of the present application can more accurately capture the phase change in the chirped pulse amplification simulation in the modeling of the chirped pulse amplification system.

[0016] Preferably, the sample data E(t) and a set of reference data with fixed parameters It is obtained by solving the generalized nonlinear Schrödinger equation and the rate equation using the distributed Fourier method and the fourth-order Runge-Kutta algorithm.

[0017] Further preferably, the sample data E(t) and a set of reference data with fixed parameters They are all M*N matrices, where M represents the number of steps of the step-by-step Fourier transform and N represents the vector dimension of the matrix pulse.

[0018] Preferably, the sample data E(t) and the reference data Perform Fourier transform (fft) to convert both from time domain to frequency domain E(ω), Among them, the calculation formula of frequency domain E(ω) is as follows: The calculation formula only needs to replace the sample data E(t) with the benchmark data That's it;

[0019]

[0020] Where e is the base of the natural logarithm, j is the imaginary unit, ω is the angular frequency, and t is time.

[0021] Further preferably, when extracting the frequency domain E(ω), the real part E of E(ω) is extracted respectively. Re (ω) and the imaginary part E lm (ω) to obtain the frequency domain amplitude A(ω) and frequency domain phase φ(ω), where the calculation formula of the frequency domain amplitude A(ω) is as follows:

[0022]

[0023] Among them, E Re (ω) and E Im (ω) are the real and imaginary parts of E(ω) respectively.

[0024] Preferably, the calculation formula of the difference phase Δ(ω) is as follows:

[0025]

[0026] in, is the reference data phase, is the frequency domain phase.

[0027] Further preferably, the normalization process adopts the following calculation formula:

[0028]

[0029] Among them, max means to find the maximum value in the array, and min means to find the minimum value in the array.

[0030] Preferably, the calculation formula for obtaining the frequency domain envelope E(ω) by the frequency domain amplitude A(ω) and the absolute phase φ(ω) is as follows:

[0031] E(ω)=A(ω)·cos(φ(ω))+A(ω)·sin(φ(ω))·1j

[0032] Here, j is the imaginary unit.

[0033] Further preferably, an inverse Fourier transform (ifft) is performed to obtain the calculation formula of the entire output E(t) of the chirped pulse amplification as follows:

[0034]

[0035] A second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the modeling method described in the first aspect of the present invention.

[0036] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0039] Figure 1 A schematic diagram of the invented fast modeling method for chirped pulse amplification system;

[0040] Figure 2 A frequency domain comparison diagram of the rapid modeling method of the chirped pulse amplification system of the present invention and method 1;

[0041] Figure 3 A time domain comparison diagram of the rapid modeling method of the chirped pulse amplification system of the present invention and method 1;

[0042] Figure 4 It is a schematic diagram of frequency domain amplitude and difference phase splicing of the fast modeling method of the chirped pulse amplification system of the present invention;

[0043] Figure 5The present invention is a flowchart of the method for rapid modeling of the chirped pulse amplification system. DETAILED DESCRIPTION

[0044] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0045] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0046] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] See also Figure 1 The present invention discloses a rapid modeling method for a chirped pulse amplification system, comprising the following steps:

[0048] S1. Obtain multiple sets of CPA sample data E(t) with different parameters and a set of benchmark data with fixed parameters

[0049] S2, the data E(t) of the sample data set and the benchmark data Convert from time domain to frequency domain E(ω), The two are extracted separately to obtain the frequency domain amplitude A(ω) and frequency domain phase φ(ω) of the pulse, as well as the frequency domain amplitude of the reference data and frequency domain phase

[0050] S3. Compare the phase φ(ω) of the sample data with the phase of the reference data Perform an unwrapping operation and extract the phase where the energy is concentrated to obtain a difference phase Δ(ω) with a small range;

[0051] S4, respectively normalizing the frequency domain amplitude A(ω) and the difference phase Δ(ω) of the sample data, and inputting the concatenated I(ω) into the neural network for training to obtain the predicted frequency domain amplitude A(ω) and difference phase Δ(ω);

[0052] S5, compare the difference phase Δ(ω) with the reference data phase The absolute phase φ(ω) is obtained by adding them together, and the frequency domain envelope E(ω) is obtained by the frequency domain amplitude A(ω) and the absolute phase φ(ω). Then an inverse Fourier transform (ifft) is performed to obtain the entire output of the chirped pulse amplification.

[0053] First, the generalized nonlinear Schrödinger equation and rate equation are solved by the distributed Fourier method and the fourth-order Runge-Kutta algorithm to obtain multiple sets of CPA sample data E(t) with different parameters and a set of benchmark data with fixed parameters. The sample data E(t) and a set of reference data with fixed parameters They are all M*N matrices, where M represents the number of steps of the step-by-step Fourier transform and N represents the vector dimension of the matrix pulse.

[0054] Then, the data E(t) of the sample data set and the benchmark data Perform Fourier transform (fft) to transform them from time domain to frequency domain and obtain E(ω), Among them, the calculation formula of frequency domain E(ω) is as follows:

[0055]

[0056] Where e is the base of the natural logarithm, j is the imaginary unit, ω is the angular frequency, and t is time.

[0057] It should be noted that The calculation formula only needs to replace the sample data E(t) with the benchmark data Therefore, the calculation formula will not be described in detail here.

[0058] In addition, when extracting E(ω) in the frequency domain, the real part E(ω) is extracted. Re (ω) and the imaginary part E Im (ω) to obtain the frequency domain amplitude A(ω) and frequency domain phase φ(ω), where the calculation formula of the frequency domain amplitude A(ω) is as follows:

[0059]

[0060] Among them, E Re 9ω) and E Im (ω) are the real and imaginary parts of E(ω) respectively.

[0061] Similarly, for the frequency domain amplitude of the reference data and frequency domain phase The same operation can also be used to obtain .

[0062] Then the phase of the sample data φ9ω) is compared with the phase of the reference data The unwrap operation is performed, and the phase range is very large. However, in the pulse edge region, the phase is meaningless because the energy is weak. After the difference, the phase range of the energy concentrated area is small, while the phase range of the weak energy and meaningless area is large. Therefore, the phase is cut to extract the phase with concentrated energy. At this time, a small difference phase Δ9ω) is obtained. The calculation formula of the difference phase Δ(ω) is as follows:

[0063]

[0064] in, is the reference data phase, is the frequency domain phase.

[0065] Then we normalize the sample data frequency domain amplitude A9ω) and difference phase Δ(ω(, respectively. By passing the two Figure 3 The above normalization process uses the following calculation formula:

[0066]

[0067]

[0068] Among them, max means to find the maximum value in the array, and min means to find the minimum value in the array.

[0069] Subsequently, a sliding window with a window size of L*N is used to obtain data, and the first set of data is to copy I(ω) L-1 times, and then slide one step at a time to obtain an M*L*N matrix, which is then input into the long short-term memory artificial neural network (LSTM) in the recurrent neural network for training to obtain the predicted frequency domain amplitude A(ω) and difference phase Δ(ω), which are then reconstructed.

[0070] The difference phase Δ(ω) is compared with the reference data phase The absolute phase φ(ω) is obtained by adding them together. Thus, the phase change in the chirped pulse amplification simulation is captured. Then, the frequency domain envelope E(ω) is obtained by the frequency domain amplitude A(ω) and the absolute phase φ(ω). The calculation formula of the frequency domain envelope E(ω) is as follows:

[0071] E(ω)=A(ω)·cos(φ(ω))+A(ω)·sin(φ(ω))·1j

[0072] Here, j is the imaginary unit.

[0073] An inverse Fourier transform (ifft) is performed to obtain the total output E(t) of the chirped pulse amplifier. The calculation formula for E(t) is as follows:

[0074]

[0075] Where e is the base of the natural logarithm, j is the imaginary unit, ω is the angular frequency, and t is time.

[0076] Therefore, the modeling method improves the phase prediction accuracy of the neural network by subtracting the frequency domain phase of the sample data from the reference frequency domain phase before training the neural network, because the phase change of the chirped pulse amplification process needs to be accurately predicted, and the phase change of the chirped pulse is complex, and the range after unwinding is large. Therefore, compared with the currently commonly used SSFM+RK4 method 1, the method of the present application is fast and has low complexity in modeling the chirped pulse amplification system; and compared with the direct data-driven method 2, the method of the present application can more accurately capture the phase change in the chirped pulse amplification simulation in the modeling of the chirped pulse amplification system.

[0077] Also, see Figures 2 to 3 , Figures 2 to 3 The following is a comparison chart of the results of simulating CPA using the method proposed in this patent and method 1. Figure 2 is the frequency domain pulse intensity and phase diagram; Figure 3 is the time domain pulse intensity and phase diagram. Figure 2 and Figure 3 The analysis shows that this method can achieve high-precision modeling of chirped pulse amplification systems, and the modeling results are consistent with those of SSFM+RK4 method 1, but with lower complexity. Therefore, in scenarios where phase changes are complex and sensitive to phase changes, the method of subtracting the phase of the sample data from the phase of the reference data and then feeding it into the neural network can be used, which can achieve a more accurate prediction of the phase.

[0078] A computer-readable storage medium according to a specific embodiment of the present invention stores a computer program, and when the computer program is executed by a processor, it is used to implement the steps of the modeling method described in any of the above embodiments of the present invention.

[0079] The above description is based on the ideal embodiment of the present invention. Through the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A fast modeling method for a chirped pulse amplification system, characterized in that: The following steps are involved: S1. Obtain multiple sets of CPA sample data E(t) with different parameters and a set of benchmark data with fixed parameters S2, sample data E(t) and benchmark data Convert from time domain to frequency domain E(ω), The transformed frequency domain is extracted to obtain the frequency domain amplitude A(ω) and frequency domain phase φ(ω) of the pulse, as well as the frequency domain amplitude of the reference data. and frequency domain phase S3. Compare the phase φ(ω) of the sample data with the phase of the reference data Perform an unwrapping operation and extract the phase where the energy is concentrated to obtain a difference phase Δ(ω) with a small range; S4, respectively normalizing the frequency domain amplitude A(ω) and the difference phase Δ(ω) of the sample data, and inputting the concatenated I(ω) into the neural network for training to obtain the predicted frequency domain amplitude A(ω) and difference phase Δ(ω); S5, compare the difference phase Δ(ω) with the reference data phase The absolute phase φ(ω) is obtained by adding them together, and the frequency domain envelope E(ω) is obtained by the frequency domain amplitude A(ω) and the absolute phase φ(ω), and then an inverse Fourier transform is performed to obtain the entire output of the chirped pulse amplification.

2. The rapid modeling method for a chirped pulse amplification system according to claim 1, characterized in that: The sample data E(t) and the reference data with a set of fixed parameters It is obtained by solving the generalized nonlinear Schrödinger equation and the rate equation using the distributed Fourier method and the fourth-order Runge-Kutta algorithm.

3. The rapid modeling method for a chirped pulse amplification system according to claim 2, characterized in that: The sample data E(t) and the reference data with a set of fixed parameters They are all M*N matrices, where M represents the number of steps of the step-by-step Fourier transform and N represents the vector dimension of the matrix pulse.

4. The rapid modeling method for a chirped pulse amplification system according to claim 1, characterized in that: Sample data E(t) and benchmark data Perform Fourier transform (fft) to convert both from time domain to frequency domain E(ω), Among them, the calculation formula of frequency domain E(ω) is as follows: The calculation formula only needs to replace the sample data E(t) with the benchmark data That's it; Where e is the base of the natural logarithm, j is the imaginary unit, ω is the angular frequency, and t is time.

5. The rapid modeling method for chirped pulse amplification system according to claim 4, characterized in that: When extracting E(ω) in the frequency domain, the real part E(ω) is extracted separately. Re (ω) and the imaginary part E Im (ω) to obtain the frequency domain amplitude A(ω) and frequency domain phase φ(ω), where the calculation formula of the frequency domain amplitude A(ω) is as follows: Among them, E Re (ω) and E Im (ω) are the real and imaginary parts of E(ω) respectively.

6. The rapid modeling method for a chirped pulse amplification system according to claim 1, characterized in that: The calculation formula of the difference phase Δ(ω) is as follows: in, is the reference data phase, is the frequency domain phase.

7. The rapid modeling method for a chirped pulse amplification system according to claim 1, characterized in that: The normalization process uses the following calculation formula: Among them, max means to find the maximum value in the array, and min means to find the minimum value in the array.

8. The rapid modeling method for a chirped pulse amplification system according to claim 1, characterized in that: The calculation formula of the frequency domain envelope E(ω) obtained by the frequency domain amplitude A(ω) and the absolute phase φ(ω) is as follows: E(ω)=A(ω)·cos(φ(ω))+A(ω)·sin(φ(ω))·1j Here, j is the imaginary unit.

9. The rapid modeling method for a chirped pulse amplification system according to claim 8, characterized in that: Performing an inverse Fourier transform (ifft) to obtain the total output E(t) of the chirped pulse amplifier is calculated as follows:

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the modeling method according to any one of claims 1 to 9 are implemented.

Images