118 results about "Automatic differentiation" patented technology

The invention discloses a three-dimensional unstructured grid adaptive refinement method and system based on machine learning, and belongs to the field of machine learning, partial differential equation solving and computational fluid mechanics simulation. Residual errors of a fluid control equation are used as a novel error indicator and a refinement criterion, imprecise flow field data and the fluid control equation are obtained by fusing coarse grids of simulation flow through a physical information neural network, and the total residual errors of the equations are conveniently calculated by using automatic differentiation after training is completed. Therefore, the coarse grid units with relatively high residual errors can be adaptively marked and refined. By matching an h-refinement scheme, Delaunay tetrahedron subdivision is executed after vertexes are strategically inserted to maintain the quality of the refined grid. According to the method, any numerical solver can be flexibly matched to carry out grid adaptive refinement, so that various typical flow problems can be solved with high precision. The method achieves better balance between calculation precision and the number of grids, and has the advantages of being simple, convenient, high in compatibility and universality and the like.

The invention relates to the cross technical field of fluid mechanics and machine learning, and discloses a particle fluid two-phase flow simulation and reverse optimization method based on deep learning. The method comprises the following steps: constructing a two-phase flow coupling simulation framework based on a fusion discrete element method and smoothed particle fluid dynamics; the drag force, buoyancy and capillary force between particles and fluid are introduced, and the calculation efficiency is improved through GPU parallel acceleration; a graph neural network is adopted to establish a particle-level dynamic interaction modeling framework, and coding and prediction of solid particles and fluid particles are independently processed; a differentiable reverse design system is further developed, and layout parameters of a flow control structure are dynamically optimized based on a gradient descent method by combining automatic differential characteristics of GNN and surrounding target functions such as flow collapse degree and centroid offset, so that effective suppression of two-phase flow destructive behaviors and system performance optimization are realized. The method has high-precision simulation and efficient optimization capabilities, and is suitable for complex two-phase flow engineering scenes such as dam burst and debris flow prevention and control.

The invention belongs to the technical field of fault prediction, and particularly discloses a physical information constraint-based digital twinning performance deduction model of a long short-term memory network and a construction method thereof, and the method comprises the steps: training a neural network model through a training set, and taking the trained neural network model as the digital twinning performance deduction model; the training set comprises a pavement physical parameter X and a pavement performance parameter Y; in the neural network model, a first LSTM network extracts time sequence characteristics of a parameter X to obtain a preliminary performance parameter predicted value # imgabs0 #; the automatic differential module adopts an automatic differential technology to calculate a partial derivative # imgabs2 # of # imgabs1 # for time t and a partial derivative # imgabs4 # of # imgabs3 # for a parameter X; and the second LSTM network obtains # imgabs6 # according to # imgabs5 # and the parameter X, and adjusts # imgabs8 # according to # imgabs7 #, so that the evolution of # imgabs8 # accords with physical priori knowledge, and physical information constraint is realized. According to the method, the digital twinning performance deduction precision in a complex environment can be effectively improved.

The present disclosure provides a computer-implemented system for automated augmentation of differential equation models. The system stores differential equations representing mechanistic behavior of physical or computational processes and constructs a hybrid computational solver by embedding a trainable universal approximator with adjustable parameters into the differential equations, where approximator outputs augment time derivatives during numerical integration. An iterative training process adjusts parameters through numerical integration, monitors integration failures, assigns infinite penalty values to loss functions when failures occur, and computes gradients using automatic differentiation otherwise. The system computes sensitivity metrics via Jacobian matrix evaluation, classifies input / output subsets as significant based on threshold-exceeding sensitivity metrics, generates a reduced approximator operating on classified subsets, and replaces the universal approximator with the reduced version to create an optimized solver.

The invention relates to the technical field of metal composite materials, in particular to a metal composite material production process dynamic simulation method based on digital twinning. Comprising the following steps: constructing a digital twin model comprising a physical entity layer, a virtual model layer and a connection layer, and establishing a Fourier neural operator model embedded with a Navier Stokes equation and a heat conduction equation constraint; real-time molten pool monitoring data are mapped into model input through a connecting layer, and advanced time step deduction is executed by using a deep learning model to predict the state of a molten pool; based on the differentiable characteristic of a physical information neural operator, calculating the gradient of a prediction state and an ideal target by using an automatic differential algorithm, generating a process parameter field through reverse iteration updating, and discretizing the process parameter field into a galvanometer control instruction; and when the risk of aggregation of the reinforced phase particles is predicted to exist, automatically generating a swing scanning strategy to realize active suppression. The method gives consideration to simulation precision and real-time performance.

Techniques for automatic parameter tuning of active road noise cancellation systems are described herein. The system can automatically search for an optimal set of algorithm parameters based on recorded data. An active road noise cancellation algorithm and simulation can be embedded in an auto-differentiation framework, which allows gradients of the algorithm parameters to guide the automatic search and calculations of the algorithm parameters.

The invention discloses a physical model discovery and reconstruction method for light beam transmission characteristics based on a sparse physical information neural network, and belongs to the technical field of nonlinear optics and deep learning intersection, and the method comprises the following steps: S1, making a sparse and low-fidelity data set; s2, establishing a candidate function library of items possibly existing in a data back model by using automatic differentiation; S3, constructing a physical information neural network model with sparse regression to discover a physical model structure; s4, coefficient fine tuning is carried out on the found physical model, and high-fidelity data is reconstructed.The method is suitable for model discovery of light beam transmission data supported by a constant coefficient or variable coefficient partial differential equation, scarce and noisy low-fidelity data can be utilized, and high-fidelity data can be obtained. A simple physical model is found according to a physical information neural network and sparse regression, and a light beam transmission dynamic process is reconstructed, so that subsequent theoretical analysis and research on light beam transmission characteristics are facilitated.

Systems and methods for using compiler transforms to transform a non-local function into a local function are disclosed. The systems and methods perform a dynamic inter-procedural analysis before performing reverse-mode automatic differentiation. The dynamic inter-procedural analysis is performed to determine a maximum set of computer program information. A non-local to local transformation is applied to the determined maximum set of computer program information, and each original instruction is mapped to an optic that is represented as an opaque closure in the transformed local function.

The invention belongs to the technical field of aero-engine design, and provides a sliding bearing lubrication gap prediction method based on a PINN neural network, and the method comprises the steps: generating a training data set of the PINN neural network, constructing the PINN neural network, employing a reverse mechanism of the neural network, calculating a partial differential operator through automatic differential, and carrying out the prediction of the lubrication gap of a sliding bearing. Customizing a dynamic pressure lubrication Reynolds equation into a loss function in a deep learning framework; and a prediction data set of the neural network model is generated, the trained PINN model and weight parameters are loaded, construction of a prediction model is completed, and an oil film pressure distribution curved surface is generated. According to the method, the accuracy of oil film pressure distribution calculation is remarkably improved, and meanwhile, the speed of numerical calculation can be remarkably increased.

The invention discloses a residual service life prediction method based on a variational auto-encoder lightweight physical information neural network. Firstly, input sensor signals are screened and preprocessed according to a signal change trend; inputting the processed data into a hidden state mapping module to extract a hidden state; inputting the time information and the extracted hidden state into a residual life prediction module, calculating a partial derivative of the residual life to the hidden state by using an automatic differential technology, and outputting a predicted residual life value; inputting the high-order partial derivative of the hidden state and the residual life into a physical information module approximation system degradation kinetic equation; a partial differential equation between sensor data and residual life is defined, a loss function is formed by weighting data fitting loss and physical constraint loss, and the physical constraint is embedded into the model to improve the reasonability of prediction; and finally, training the model to obtain a final predicted value. According to the method, the number of parameters is remarkably reduced, and meanwhile better prediction performance and higher interpretability are shown.

The invention relates to a power transmission line positive sequence and zero sequence parameter identification method based on an improved physical information neural network, and the method comprises the steps: carrying out the processing of double-end fault recording data of a line, and dividing the data into normal operation, fault transient state and fault steady state of the line; constructing a differential equation taking the line positive sequence parameter as a coefficient, and calculating the coefficient of the differential equation by using the RBF-PINN1 and the normal operation data of the line to obtain the line positive sequence parameter; and constructing a differential equation taking the line zero-sequence parameter and the fault distance as coefficients, and calculating the coefficient of the differential equation by using the RBF-PINN2 and the line fault steady-state data to obtain the line zero-sequence parameter and the fault distance. Neural network training is guided by using the power transmission line physical model, the requirement for large-scale data is avoided, and the data acquisition difficulty is remarkably reduced; voltage and current differentials are accurately calculated by using an automatic differential function of the neural network, so that the accuracy of line parameter identification is remarkably improved; and meanwhile, the anti-noise capability is remarkably improved, so that the high precision can still be kept in a noise environment.

The invention discloses a bridge damping ratio identification method under a dimensionless control equation based on PINNs, and relates to the technical field of bridge damping ratio identification, the bridge damping ratio identification method comprises the following steps: carrying out network mapping on an input variable to obtain an output displacement; performing automatic differentiation on the output displacement to obtain each differential item; performing dimensionless processing on each parameter and differential item, and constructing a dimensionless free vibration differential equation containing an unknown damping ratio; loss is calculated, and a total loss function is constructed; and updating network parameters and trainable variables, and judging a training end point according to a network convergence mechanism. According to the bridge damping ratio identification method based on the PINNs under the dimensionless control equation, the rigidity and the linear density of the bridge are implied in dimensionless parameters, the magnitude order difference between all differential term coefficients is fundamentally eliminated, and bridge damping ratio identification based on the PINNs method is achieved.

The invention relates to the technical field of environment monitoring, and particularly discloses an air quality prediction optimization method and system based on deep learning, a space-time diagram structure is constructed by taking a monitoring station as a node, pollutant concentration, meteorological elements and geographic coordinates are fused to form node features, and a dynamic adjacency matrix is generated based on real-time wind direction and wind speed; spatial dependency feature representation is obtained through multi-level spatial feature aggregation; after a prediction concentration field is obtained through feature mapping, spatial gradient and curvature features are calculated through the automatic differential technology, the relative contribution degree of the advection transmission and diffusion process is analyzed in combination with wind field data, the consistency of a prediction result and a physical rule is verified, and a physical consistency residual error is obtained; a composite optimization target is constructed based on the data fitting difference and the physical consistency residual error, and parameter configuration meeting physical rule constraints is obtained through alternate optimization; and finally, processing real-time monitoring data by using the optimized parameters, and outputting an air quality prediction result conforming to a physical rule.

The invention discloses a transient electromagnetic rapid inversion method based on automatic differentiation, which belongs to the technical research field of geophysical electromagnetic data processing and analysis, and comprises the following steps of: expressing a transient electromagnetic forward modeling equation and an inversion objective function by using a forward calculation graph realized in an automatic differentiation framework; and the model gradient is accurately and efficiently calculated by using an adaptive moment estimation optimizer and a chain rule so as to update model parameters. According to the invention, an efficient gradient operation method automatic differential is innovatively applied to the transient electromagnetic field, and a self-adaptive moment estimation method which is very adaptive to the automatic differential is used as an optimization algorithm method to carry out the iterative updating of a model. The problem that the rate and the precision are not considered at the same time due to gradient calculation of existing traditional transient electromagnetic inversion is solved. The method has an important application prospect in engineering actual transient electromagnetic exploration.

The invention discloses a multi-scale structural stiffness optimization design method based on neural network re-parameterization, which comprises the following steps: microstructure parameterization modeling: carrying out parameterization modeling on a microstructure by adopting a level set method, and controlling the volume fraction and geometric configuration of the microstructure by adjusting a cutting height parameter; proxy model construction: establishing a mapping relation between cutting height parameters and microstructure equivalent mechanical properties by using a neural network to form a high-precision proxy model; neural network re-parameterization design: constructing a neural network optimizer, taking coordinates of each discrete unit in a design domain as input, and outputting corresponding cutting height parameters; neural network parameters are updated through automatic differentiation and back propagation, and continuous optimization of design variables is achieved; and a multi-scale topological optimization process: calculating a global stiffness matrix and structural flexibility in combination with an agent model and finite element analysis, and obtaining an optimal multi-scale structural topology meeting volume constraints through loss function minimization iterative optimization.

The invention relates to the technical field of seismic engineering, and provides a method and system for solving a seismic wave propagation equation based on a physical information neural network, and the method comprises the steps: determining a space domain, a time domain, an initial condition and a boundary condition of a one-dimensional seismic wave equation; initial boundary value coordinate points are randomly extracted to serve as a training data set constraint network, and Latin hypercube sampling is utilized to generate space-time domain configuration points to meet partial differential equation constraint; a physical information neural network model is established, after space-time coordinates are input, equation residual errors are calculated through automatic differential, and initial boundary value loss and physical residual errors are combined to construct a composite loss function for training; and proposing a self-adaptive region sampling strategy based on residual errors, resampling a high residual error region according to an average residual error value of the configuration points in a training period, and iteratively optimizing network parameters after a newly added point and an initial configuration point are fused. According to the method, by dynamically enhancing the sampling density of a high-error region, the L2 relative error of a prediction solution is reduced by one order of magnitude, the network is effectively prevented from falling into local optimum, the convergence efficiency is remarkably improved, grid discretization or prior data support is not needed, and a high-precision meshless solution scheme is provided for a one-dimensional seismic wave equation.

A method for evaluating and / or adapting one or more technical models related to an industrial and / or technical process, as well as a corresponding system and computer program are provided. The method comprises obtaining (S1) a fully or partially non-causal modular parametric model of the industrial and / or technical process, the parametric model comprising at least one physical sub-model and at least one neural network sub-model, including obtaining one or more parameters of the parametric model. The method further comprises generating (S2) a system of differential equations based on the parametric model, and simulating (S3) dynamics of one or more states of the industrial and / or technical process over time based on the system of differential equations. The method also comprises applying (S4) reverse mode automatic differentiation to the system of differential equations while simulating the industrial and / or technical process to generate an estimate representing an evaluation of the industrial and / or technical process model.

The application discloses a method for discovering control equations from scarce and noisy data based on physics. The application combines a physical information neural network and a sparse regression method to discover the partial differential control equations of a dynamic system from scarce and noisy data. Firstly, the size of the candidate function library is effectively reduced through a dimension verification method. Then, the powerful nonlinear fitting capability and automatic differentiation characteristics of a deep neural network are used to model a physical system and calculate candidate functions. Finally, the form of the control equation and the coefficients of the equation are obtained through sparse regression, and the coefficients are fine-tuned through a DNN. The method can not only discover control equations from data, but also obtain a network model to realize response prediction of a dynamic system. The method is simple, efficient, high-precision and highly universal, and can be widely applied to physical knowledge mining, modeling and reasoning of complex dynamic systems.

The invention discloses a weighted exponential decay instantaneous phase inversion method based on automatic differentiation. Under the framework of automatic differential, the underground velocity model can be inversed by using phase and waveform information at the same time, and the advantages of constructing an initial model at the early stage of inversion and improving the resolution of the model at the later stage of inversion are combined. The method comprises the following steps: firstly, performing Hilbert transform on seismic wave data, extracting index instantaneous phase information of observation data and simulation data, and constructing a weighted instantaneous phase inversion objective function by introducing an attenuation coefficient and combining waveform information; secondly, carrying out back propagation on the loss of the objective function based on an automatic differential framework, and solving an update gradient; and finally, updating the speed model by using an SDG optimizer. According to the method, a layered multi-scale inversion effect can be realized by adjusting attenuation factor values, an objective function is constructed by weighting exponential attenuation phase information and waveform information, a better initial model is constructed before a conventional multi-scale full-waveform inversion process, the problem of cycle jump of full-waveform inversion is relieved, and the inversion efficiency is improved. And finally, a high-precision inversion result is obtained.

The invention belongs to the crossing field of computational mechanics and deep learning, and particularly discloses a heterogeneous composite material equivalent elastic modulus prediction method based on cross-scale differential-integral coupling, and the method comprises the steps: building a multi-scale elastic modulus prediction equation; constructing a cross-scale coupling differential equation set; performing data sampling on the multi-scale elastic modulus prediction equation to generate a training sample; designing a multi-channel neural network architecture; calculating an elastic modulus predicted value and an integral operator original function of each order of the defect field function through neural network forward propagation; through an automatic differential technology, a high-order derivative is accurately calculated, and a differential operator is obtained; driving the neural network to perform back propagation through a composite loss function to update network parameters; when the convergence condition is met, the output of the neural network is the solution of the multi-scale elastic modulus prediction equation. By constructing a macroscopic-microscopic dual-scale differential constraint system and a defect field integral error suppression framework, the bottleneck of traditional experimental test and numerical simulation in cross-scale mechanical analysis is effectively broken through.

A digital twin modeling method and device for motors based on multi-field coupled PINN (Partial Differential Equations) belongs to the field of intelligent manufacturing and advanced control technology for motors. Its key features include: acquiring stator current, voltage, rotor position, and vibration signals of the motor to construct a full-domain multimodal sensing layer; constructing a multi-field coupled PINN model based on automatic differentiation; and embedding the multi-field partial differential equations (PDEs) inside the motor as prior knowledge into the network structure of the PINN model. By utilizing PINN technology, the physical laws of PDEs are integrated into the neural network, ensuring both millisecond-level inference speed and physical consistency in areas lacking sensor data, achieving both high fidelity and real-time performance. It enables precise virtual sensing of unmeasurable rotor permanent magnet temperature and local magnetic flux density saturation within the motor, achieving full-domain observability. Through edge-cloud collaboration, it can automatically evolve as the physical entities of the motor age (e.g., resistance changes, magnet demagnetization), maintaining model accuracy throughout its entire lifecycle.

Examples including a system described herein implement a novel liquid time constant neural network (LTC-NN) based architecture to recover an underlying model of physical dynamics from real world data. The automatic differentiation property of LTC-NN nodes overcomes problems associated with low sampling rate, the input dependent time constant in the forward pass of the hidden layer of LTC-NN nodes creates a massive search space of implicit physical dynamics, the physics model solver based data reconstruction loss guides the search for the correct set of implicit dynamics, and drop out in dense layer ensures extraction of the sparsest model. Further, to account for perturbation timing error, the LTC-NN based architecture of the system utilizes dense layer nodes to search through input shifts that results in the lowest reconstruction loss.

The application discloses a steel bridge deck roller compacted asphalt concrete construction simulation method based on BIM, and relates to the technical field of simulation. The method first analyzes the geometric boundary of the BIM model and constructs an adaptive space-time sampling point set; then constructs a deep neural network introducing Fourier feature mapping, maps low-dimensional space-time coordinates into high-dimensional features to capture high-frequency physical changes; then establishes a composite loss function containing heat conduction, thermal coupling and boundary condition residual error, and uses automatic differentiation technology and an adaptive weight algorithm based on gradient statistics for unsupervised training; finally, the trained lightweight model is integrated into the BIM rendering engine. The application solves the problems of long time consumption of traditional finite element simulation calculation, inability of real-time interaction, and poor generalization ability of pure data-driven AI due to lack of internal measured data, and realizes real-time, high-precision dynamic simulation of the temperature field and stress field of the steel bridge deck pavement.

The invention discloses a functionally graded material structure optimization method based on a physical information network and deep regression. The method comprises the steps of 1, building a physical information network, calculating strain and stress through automatic differential, and building physical constraints in combination with a constitutive equation and an equilibrium equation; the deviation between the network prediction and the physical law is quantified through a mean square error to ensure that the prediction result conforms to the mechanics principle; 2, building a deep regression network, and optimizing and training the deep regression network through the mapping relation; an optimal expression is searched through gradient descent, and an analyzable volume fraction function model is finally generated and used for material distribution optimization; and step 3, adopting an alternating iteration strategy, finally outputting an expression of VA (x, y, z), verifying Young modulus distribution accuracy through Voigt homogenization, and realizing automatic optimization design of the functionally graded material. According to the method, the optimal distribution of each component of the functionally graded material structure is automatically obtained by adopting a physical information neural network method, so that the performance of the functionally graded structure is optimized.

This invention discloses a method and system for solving the performance of a gas turbine engine by integrating differentiable operators and static computation graphs. The method includes: constructing differentiable component operators: reconstructing the input-output mapping relationship of the core components of the gas turbine engine into a globally differentiable surrogate model, which possesses analytical gradient solving capability; constructing the overall engine static computation graph: utilizing a programming framework supporting automatic differentiation, establishing a general graph construction mechanism decoupled from the engine topology, which automatically instantiates the corresponding static computation graph based on the input engine topology description; and reverse-mode automatic differentiation solving: calculating the residual vector through forward propagation, and using reverse-mode automatic differentiation technology to propagate the gradient flow backward along the static computation graph, analytically obtaining the Jacobian matrix required for Newton iteration, and completing the solution of the engine's overall performance residual equation. This invention achieves efficient, accurate, and stable simulation of gas turbine engine performance.

The invention discloses a complex river network flow prediction method based on a diffusion space-time diagram neural network guided by physical information, and relates to the technical field of intelligent water conservancy and hydrological time sequence prediction. According to the method, a diffusion space-time diagram neural network (PI-Diffusion STG) guided by physical information is constructed aiming at the problems of lack of physical consistency and insufficient long-distance space-time dependence capture capability of an existing data-driven model. According to the model, a bidirectional graph diffusion convolution module is designed, forward-flow hydraulic conduction and reverse-flow jacking effects in a river network are simulated through forward diffusion and backward diffusion respectively, and bidirectional spatial-temporal characteristics are effectively extracted; performing multi-step traffic prediction by adopting a sequence-to-sequence architecture; meanwhile, a Saint-Venant equation set describing fluid mechanics is used as a physical constraint regularization term to be deeply fused into a loss function, and a physical residual error is calculated by utilizing a hybrid strategy combining automatic differential and finite difference on a graph. According to the method, the prediction precision of the complex river network under extreme conditions can be remarkably improved, the prediction result is ensured to accord with the law of conservation of momentum and quality, and the method has relatively high robustness and physical interpretability.

The application discloses a kind of based on deep learning's full-waveband microwave radiation transmission simulation method and system, method includes: based on global reanalysis data and surface type product, obtain atmospheric profile and surface parameter, adopt near neighbor search and de-redundancy, construct representative "surface parameter-atmospheric profile" database;Using high-precision microwave radiation transmission mode, simulate and calculate the atmospheric top layer brightness temperature covering full-waveband microwave, construct training sample pair;Calculate the correlation coefficient between all output channels, and select the representative channel subset with reduced number from threshold value;Build a fully connected neural network, with the brightness temperature of the representative channel subset as the intermediate feature layer, and use a phased composite loss function strategy for training;Surface parameters and atmospheric profile are input into the trained model to obtain full-waveband brightness temperature prediction results, and / or using automatic differentiation mechanism, the trained model is used as a differentiable operator to calculate the Jacobian matrix of the output brightness temperature relative to the input parameters.

The invention discloses a catastrophe prediction and risk assessment method for a defective road-stratum system based on a mechanical-data dual-drive model. The method relates to the technical field of road-stratum system catastrophe prediction and risk assessment. Comprising the steps that road collapse statistical data and engineering settlement measured data are collected, and a data set is constructed through preprocessing; a mechanical analysis model is established, road internal force and deflection are calculated based on a viscoelastic foundation beam theory, a Merchant rheological model and a modified Cambridge model are fused to solve stratum stress and cavity evolution, and a cooperative response control differential equation and boundary conditions are formed; a physical information neural network is constructed, a mechanical loss function is set, and a catastrophe agent model is obtained through automatic differential training; training an optimization network by using the data set to obtain a mechanical-data dual-drive prediction agent model; and introducing disaster-causing factor probability characteristics, and quantifying disaster probabilities under different defect conditions through probability simulation and risk assessment.

The application discloses a kind of compilation methods for automatically mixed precision optimization procedure, first the preprocessing of the source code file of the program to be optimized is carried out based on the static error analysis technique of chain automatic differentiation, then the precision sensitivity of floating point variable in program is analyzed using the static error analysis technique based on the chain automatic differentiation, precision insensitive variable is determined, and variable information is stored in JSON file;Again, the source code file of the program to be optimized is used as input, the file is traversed by using variable information search tool, the information of all variables in the program is obtained to form a variable configuration file, and a configuration file of variable precision search space is formed according to the precision configuration scheme of the current program;Using the result of error analysis, the variable precision search space is reduced, and the optimized variable precision search space file is formed;The application can solve the technical problem that the execution efficiency of the optimized program cannot be improved by the automatic mixed precision optimization technology based on error analysis.

The invention discloses a truss structure nonlinear buckling stability optimization design method based on a finite mass point method and automatic differentiation, and the method comprises the steps: constructing a parameterized truss model, and taking the cross-sectional area and / or node coordinates of a rod piece in the parameterized truss model as design variables; constructing an optimization objective function for determining the nonlinear buckling stability performance of the structure as an objective; and on the basis of the parameterized truss model, constructing a corresponding finite mass point method analysis model, and performing iterative optimization on the optimization objective function in the process of simulating nonlinear static response to buckling in the finite mass point method analysis model in combination with automatic differential software, so as to output a final design variable as the optimal truss structure design. The method provided by the invention can effectively solve the problem of buckling stability optimization of the truss structure which becomes abnormally complex due to geometric nonlinearity and path dependence characteristics.