System and program

Abstract

To provide a system and a program that can industrially advantageously perform, on the basis of a first structure, information processing for designing a second structure.SOLUTION: A system for designing a second structure on the basis of a first structure includes: means for designing the second structure using material area information indicating an area of each constituent material of the first structure and characteristic information indicating a characteristic of the first structure; and means for designing the second structure using required specifications for the second structure, in which the means designs two or more second structures having different characteristics.SELECTED DRAWING: Figure 8

Description

[Technical Field]

[0001] The present invention relates to a system and a computer program that are useful for designing structures such as motors. [Background technology]

[0002] In recent years, interior permanent magnet synchronous motors (IPMSMs) have been widely adopted as motors for driving electric vehicles and industrial robots, due to their high output, efficiency, and reliability. The optimal shape design of IPMSMs requires repeated finite element analysis (FEA), which poses a challenge due to the long computational time. To address this issue, surrogate models utilizing machine learning and deep learning have attracted attention, enabling optimal design in a short period of time without using FEA. When applying machine learning to motor design, the shape of an IPMSM must be numerically represented. Typical numerical representations include parameter representations, which treat the dimensions defining the shape as vectors, and material representations, which specify materials on polar coordinates. While design parameter representations are the simplest method for specifying each dimension of a shape, they have the disadvantage that the shapes they can represent depend on a reference shape. While image representations have the advantage of being able to represent various topologies, they also have the disadvantage of being difficult to generate and generating unmanufacturable shapes due to the large degree of freedom in the design space.

[0003] Non-Patent Document 1 discloses an automated design system for interior permanent magnet synchronous motors. When automatically designing a structure such as a motor, it is desirable to prevent the generation of improper shapes such as collapsed shapes. This automated design system can prevent the generation of improper shapes by adopting a representation format based on a graph structure that limits the degree of freedom of expression.

[0004] However, the representation format based on the graph structure in Non-Patent Document 1 uses a vector representation that focuses on the vertex coordinates of the structure, and therefore, since there are many parameters to add in the shape modification operation, there is still an issue that inappropriate shapes such as collapsed shapes are generated.

[0005] Therefore, when automatically designing a second structure from information about a first structure such as a motor, it is desirable to prevent an inappropriate shape such as a collapsed shape from being generated. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Yusei Shimizu, "Characteristic Prediction of IPMSM Using Graph Structure and Deep Learning", Institute of Electrical Engineers of Japan Research Meeting Materials. RM, Institute of Electrical Engineers of Japan Rotating Machinery Research Meeting [ed.], 2023, 53-58, 2023-08-25 Summary of the Invention [Problem to be solved by the invention]

[0007] An object of the present invention is to provide a system and a program that can industrially advantageously perform information processing for designing a second structure based on a first structure. [Means for solving the problem]

[0008] As a result of intensive research to achieve the above-mentioned object, the inventors have discovered that a system for designing a second structure based on a first structure, which system is characterized by comprising means for designing the second structure using material domain information indicating the domains of each constituent material of the first structure and property information indicating the properties of the first structure, minimizes the additional parameters in the shape modification operation and prevents the generation of an inappropriate shape, and has found that such a system can solve the above-mentioned conventional problems in one fell swoop. Furthermore, after obtaining the above findings, the present inventors conducted further studies and completed the present invention.

[0009] That is, the present invention relates to the following inventions. [1] A system for designing a second structure based on a first structure, characterized in that the system includes a means for designing the second structure using material domain information indicating the domains of each constituent material of the first structure and property information indicating the properties of the first structure. [2] The system according to [1], further comprising means for designing the second structure using the required specifications for the second structure. [3] The system according to [1], wherein the means designs two or more of the second structures having different characteristics. [4] The system according to [1], wherein the material domain information includes air domain information. [5] The system according to [1], wherein the material domain information includes magnetic material domain information. [6] The system according to [1], wherein the material region information indicates each region for each material property of each constituent material. [7] The system according to [1], wherein the characteristic information includes information indicating physical characteristics. [8] The system according to [1], wherein the structure is an electrical device. [9] The system according to [1], further comprising an evaluation means for evaluating the characteristics of the first structure from the material domain information when the material domain information is received from a user.

[10] The system described in [9] above, which learns the relationship between the material domain information and the characteristics, predicts the characteristics from the material domain information of the user through the learning, and outputs the predicted evaluation value to the user.

[11] A program that causes a system for calculating the characteristics of a structure to function as a means for learning the relationship between the characteristics using material domain information indicating the domains of each constituent material of the structure, predicting the characteristics from the material domain information received from a user through said learning, and outputting the predicted evaluation value to the user. [Effects of the Invention]

[0010] The system and program of the present invention can industrially advantageously perform information processing for designing a second structure based on a first structure. [Brief explanation of the drawings]

[0011] [Figure 1] Figure 1 is a diagram of the design system. [Figure 2] FIG. 2 is a perspective view of the motor. [Figure 3] FIG. 3 is a diagram showing the topology of one pole of the rotor. [Figure 4] FIG. 4 is a flowchart of the graph data generation process. [Figure 5] FIG. 5 is an explanatory diagram of the graph data. [Figure 6(A)] FIG. 6(A) shows variations of the evaluator. [Figure 6(B)] FIG. 6(B) shows a variation of the evaluator. [Figure 7(A)] FIG. 7A is a diagram showing an example of a design system for generating an image. [Figure 7(B)] FIG. 7B is a diagram showing an example of an image of a collapsed shape. [Figure 8] FIG. 8 is a diagram for explaining area vectors focusing on the area of ​​a structure. DETAILED DESCRIPTION OF THE INVENTION

[0012] The present invention is a system for designing a second structure based on a first structure, and is characterized by comprising means for designing the second structure using material domain information indicating the domains of each constituent material of the first structure and property information indicating the properties of the first structure.

[0013] The material region information used in the present invention is not particularly limited as long as it includes three or more pieces of coordinate information and constituent materials. Examples of the material region information include a combination of three pieces of coordinate information and constituent material information, and a combination of four pieces of coordinate information and constituent material information. More specific examples include the material region information shown in FIG. 8. Examples of the coordinate information include known coordinates, more specific examples include polar coordinates, cylindrical coordinates, and Cartesian coordinates. Examples of the constituent material information include constituent material symbols. The constituent material symbols are not particularly limited as long as they represent the constituent materials, and examples of the constituent material symbols include one or a combination of two or more symbols selected from numbers, letters such as the alphabet, and other symbols. Examples of such structures include motors, piezoelectric actuators, RFID (Radio Frequency Identification), cables, high-frequency preheaters, capacitors, magnetic heads, magnetic shields, magnetic gears, magnets, circuit breakers, sensors, electrical heating devices, electrolytic plating, electromagnetic forming, electromagnetic retarders, electromagnetic relays, transformers / reactors, bus bars, generators, heaters, bearings, magnetic couplings, induction heating devices, induction machines, linear solenoids / linear actuators, and wireless power supply systems.

[0014] Furthermore, the present invention preferably further comprises a means for designing the second structure using required specifications for the second structure. According to such a preferred embodiment, the designer's intentions can be more accurately reflected in the design of the second structure. The required specifications include, for example, required operating points such as speed and torque, maximum phase current, maximum terminal voltage, maximum speed, size, weight, cost, torque density, current density, efficiency, loss, maximum temperature, mechanical strength, and anti-demagnetization performance.

[0015] In the present invention, it is also preferable that the means designs two or more of the second structures having different characteristics. According to such a preferred embodiment, the designer can select the most optimal second structure from a plurality of design proposals that have a trade-off relationship. Examples of the characteristics include those described in the required specifications.

[0016] In the present invention, it is also preferable that the material region information includes air region information. According to such a preferred embodiment, information including the space of the first structure and / or the second structure can be more easily processed. Examples of the air region information include information including a material information symbol representing air.

[0017] In the present invention, the material region information preferably includes magnetic material region information. According to such a preferred embodiment, information processing including the material properties of the first structure and / or the second structure can be more easily performed. Examples of the magnetic material region information include material information symbols that indicate magnetic materials.

[0018] In the present invention, the material region information preferably indicates regions for each material property of each constituent material. According to such a preferred embodiment, more detailed information processing can be performed, including evaluation of the characteristics of the first structure and / or the second structure. Examples of the regions for each material property include regions for magnetic permeability, saturation magnetic flux density, residual magnetic flux density, magnetization, coercive force, loss factor, permittivity, resistivity, and conductivity.

[0019] In the present invention, it is also preferable that the characteristic information (hereinafter simply referred to as "characteristics") includes information indicating physical characteristics. According to such a preferred embodiment, the durability and physical stability of the second structure are further improved, and the generation of an inappropriate shape such as a collapsed shape can be further suppressed. Examples of the physical characteristics include the physical characteristics described in the required specifications.

[0020] In the present invention, the structure is preferably an electrical device, such as a motor, a piezoelectric actuator, an RFID (Radio Frequency Identification), a cable, a high-frequency preheater, a capacitor, a magnetic head, a magnetic shield, a magnetic gear, a magnet, a circuit breaker, a sensor, an electric heating device, electrolytic plating, electromagnetic forming, an electromagnetic retarder, an electromagnetic relay, a transformer / reactor, a bus bar, a generator, a heater, a bearing, a magnetic coupling, an induction heating device, an induction machine, a linear solenoid / linear actuator, or a wireless power supply system.

[0021] In addition, the present invention preferably includes an evaluation means for evaluating the characteristics of the first structure based on the material domain information when the material domain information is received from a user. According to this preferred embodiment, particularly nonlinear characteristics of the first structure and / or the second structure can be evaluated in more detail. Examples of the evaluation means include finite element analysis.

[0022] In addition, in the present invention, it is preferable to learn the relationship between the material domain information and the properties, predict the properties from the material domain information of the user through the learning, and output the predicted evaluation value to the user. According to this preferred embodiment, the properties of the first structure and / or the second structure can be evaluated in a shorter time. Examples of the learning and prediction means include machine learning techniques such as support vector regression, Gaussian process regression, random forest, gradient boosting decision tree, neural network, multilayer perceptron, convolutional neural network, Transformer, Vision Transformer, MLP-Mixer, and graph neural network.

[0023] The present invention will be specifically described below with reference to the drawings, but the present invention is not limited to these specific examples.

[0024] 1 shows a design system 100 according to a preferred embodiment. The design system 100 designs the shape of a structure. The shape to be designed is, for example, a physical two-dimensional or three-dimensional shape of a tangible structure. The shape may be the shape of a part of the structure or the shape of the entire structure.

[0025] As an example, the structure may be an electric machine. Here, electric machines are a general term for rotating electric machines (rotating machines) such as motors and generators, and stationary electric machines (stationary machines) such as transformers, rectifiers, and switching control devices. Electric machines are machines whose electromagnetic properties can be evaluated in the design stage.

[0026] The structure may, for example, comprise a magnet, for example an electric machine such as a motor.

[0027] The design system 100 according to the embodiment includes a generator 110 and an evaluator 120. The generator 110 generates a shape of a structure. The evaluator 120 evaluates characteristics of the structure based on the shape generated by the generator 110.

[0028] The design system 100 may be configured by one or more computers having a processor and a memory. The processor may be a CPU or a GPU. The memory is connected to the processor. The memory may include, for example, a primary storage device and a secondary storage device. The primary storage device may be, for example, a RAM. The secondary storage device may be, for example, a hard disk drive (HDD) or a solid-state drive (SSD). The memory may include a computer program executed by the processor. The processor reads and executes the computer program stored in the memory. The computer program has program code for causing the computer to operate as the design system 100. That is, the computer program has program code for causing the computer to operate as the generator 110 and the evaluator 120.

[0029] The design system 100 can output an optimized shape by repeatedly generating and evaluating a shape. For example, the design system 100 uses the determiner 150 to determine whether the characteristics evaluated (predicted) by the evaluator 120 satisfy predetermined requirements, and if the requirements are satisfied, outputs a shape having those characteristics as an optimized structure. If the characteristics evaluated by the evaluator 120 do not satisfy the predetermined requirements, the design system 100 regenerates and evaluates the shape. The design system 100 automatically outputs an optimized shape by repeatedly generating and evaluating various shapes.

[0030] The characteristics evaluated by the evaluator 120 are characteristics of the structure having the shape generated by the generator 110. The characteristics may be, for example, characteristics of a rotor or a stationary motor. The characteristics may be, for example, electromagnetic characteristics. If the structure is a motor, the characteristics may be motor characteristics. The motor characteristics may include, for example, any one or a combination of the speed-torque characteristics of the motor, the efficiency of the motor, torque ripple, and iron loss.

[0031] In the following description, the design system 100 according to the embodiment is used to design a motor, as an example. Motors are used in various products such as electric vehicles, drones, aircraft, and industrial robots. The motor to be designed by the design system 100 is, as an example, an interior permanent magnet synchronous motor (IPMSM).

[0032] As shown in Fig. 2, interior permanent magnet synchronous motor 10 is a type of motor that uses permanent magnets 15. Interior permanent magnet synchronous motor 10 includes stator 11 and rotor 12 provided inside stator 11. Permanent magnets 15 are embedded in rotor 12. Interior permanent magnet synchronous motor 10 shown in Fig. 2 is, as an example, an 8-pole, 48-slot distributed winding IPMSM.

[0033] The stator 11 is made of electromagnetic steel and has multiple slots 16 formed in the circumferential direction. Air exists in the slots 16. Therefore, when designing the stator 11, two types of materials must be taken into consideration: electromagnetic steel and air.

[0034] Rotor 12 is constructed by embedding permanent magnets 15 in a rotor body made of electromagnetic steel. In rotor 12, regions (holes) where air exists are formed in positions adjacent to permanent magnets 15. Therefore, when designing rotor 12, three types of materials must be considered: electromagnetic steel, permanent magnets, and air.

[0035] Here, the object of design is rotor 12, and the structure of stator 11 (stator structure) is assumed to be predetermined. The external shape of rotor 12 is also assumed to be predetermined. Therefore, the object of design here is the shape and arrangement of the magnets and air in rotor 12. Note that the external shapes of stator 11 and rotor 12 may also be subject to design.

[0036] Furthermore, since each pole of the rotor 12 has the same structure, when designing the rotor 12, it is sufficient to design one pole of the rotor 12.

[0037] 3(A) to 3(C) show examples of two-dimensional topology for one pole of the rotor 12. The topologies shown in Fig. 3(A) to 3(C) differ in the position, number, shape, etc. of the permanent magnets and holes (air).

[0038] In the design system 100 according to the embodiment, the generator 110 generates and outputs graph data 200 that indicates the two-dimensional shape of one pole of the rotor 12 shown in Fig. 3. The evaluator 120 acquires the graph data 200 output from the generator 110, and evaluates the motor characteristics of a motor (IPMSM) that includes the rotor 12 having the generated shape, based on the graph data 200.

[0039] FIG. 4 shows an example of the procedure of the process S400 for generating the shape of a structure, which is executed by the generator 110. As described above, the generator 110 according to the embodiment generates the shape of the rotor 12 as graph data 200, which is represented by a graph. A graph includes nodes (vertices) and edges (sides). Although a graph has less freedom of expression than an image, it still has sufficient freedom of expression to represent a two-dimensional or three-dimensional shape of a structure. Therefore, a graph is suitable as a representation format for the shape to be generated, and can prevent the generation of an inappropriate shape, such as a collapsed shape.

[0040] A matrix that represents a graph is, for example, an adjacency matrix or a connection matrix. An adjacency matrix indicates the adjacency relationship between nodes in a graph due to edges (whether nodes are adjacent to each other via edges). An adjacency matrix is ​​a representation format of a graph that focuses on the adjacency relationship between nodes. On the other hand, a connection matrix is ​​a representation format of a graph that focuses on the connection relationship between nodes and edges. A connection matrix indicates the connection relationship between nodes and edges (whether nodes and edges are connected).

[0041] FIG. 5 shows an example of a graph representing a structure, focusing on subregions included in the structure. Here, a subregion is a two-dimensional or three-dimensional partial region that constitutes the structure. A subregion is a small region obtained by dividing a structure into multiple parts, like an element in the finite element method. The shape of a subregion may be one type, or multiple types. A subregion can be set for each material of the structure.

[0042] In FIG. 5, the nodes of the graph indicate subregions included in the rotor 12 (structure). Also in FIG. 5, the nodes of the graph indicate subregions that represent the magnet regions included in the rotor 12 and subregions that represent the air regions present in the rotor 12. Also, the edges of the graph indicate that the subregions represented by the nodes are adjacent to each other. In the graph representation shown in FIG. 5, the electromagnetic steel region that represents most of the rotor 12 is not represented, and the magnet and air regions are represented by nodes and edges. By omitting the representation of the electromagnetic steel region, the data size can be reduced.

[0043] Returning to Fig. 4, the generation process S400 by the generator 110 will be described. First, the generator 110 selects a generation topology (step S401). A plurality of selectable generation topologies are set in advance in the generator 110. The plurality of selectable generation topologies are, for example, the three rotor topologies shown in Figs. 3(A) to (C).

[0044] The generation topology may be selected by accepting a user operation to select the generation topology, or the generator 110 may automatically determine a generation topology from among a plurality of generation topologies based on an appropriate rule or randomly.

[0045] Each of the multiple pre-set generating topologies has set therein an adjacency matrix (a matrix representing a graph) and values ​​other than node position coordinates in the area vector (for example, material data associated with each node). Therefore, when a generating topology is selected in step S401, the initial values ​​of the adjacency matrix and area vector are determined according to the selected generating topology (step S402). In other words, the selection of the generating topology determines all of the graph data 200 indicating the shape of the rotor 12 (structure) except for the node position coordinates. In this way, the generating topology indicates graph data in which only the node position coordinates are variables (undetermined values). Note that the number of nodes in each generating topology may be a fixed value.

[0046] The generator 110 generates node position coordinates in the selected generated topology (step S403). By generating the position coordinates of each node, graph data 200 (generated data) consisting of an adjacency matrix and an area vector is generated. An example of a method for generating node position coordinates will be described later.

[0047] The generator 110 verifies the generated graph data (generated data) (step S404). This verification is performed based on predetermined verification criteria to determine whether the generated graph data (generated data) properly represents the shape of the rotor 12. The verification criteria are, for example, criteria that indicate when the shape is inappropriate. Examples of criteria that indicate when the shape is inappropriate include "each edge intersects," "the node position coordinates are outside the specified area," and "the magnet has a shape other than a rectangle."

[0048] Edges (straight lines that define the shape of a structure) should not intersect, so if any edges intersect, the shape is inappropriate.

[0049] The node position coordinates must be within a predetermined designated area in each generated topology, so if they are outside the designated area, the shape is inappropriate. The designated area can be set within the rotor 12.

[0050] The magnets should be rectangular, so if the magnets are shaped other than rectangular, they are improperly shaped.

[0051] If the verification in step S404 determines that the generated shape is inappropriate (problematic) (step S405), the process returns to step S403, and node position coordinates in the selected generated topology are regenerated.

[0052] If the verification in step S404 determines that the generated shape is appropriate (no problem) (step S405), the generator 110 outputs the generated graph data 200 (generated data). Therefore, only appropriate graph data 200 is output from the generator 110. The output graph data 200 is provided to the evaluator 120.

[0053] Fig. 6 shows a variation of the evaluator 120 shown in Fig. 1. The evaluation of the characteristics of the structure based on the graph data 200 may be either Fig. 6(A) or Fig. 6(B).

[0054] 6(A) includes a first characteristic evaluation model 121 (trained model) that has been machine-learned so as to output the characteristics of a motor equipped with a rotor 12 having a shape indicated by graph data 200 when the graph data 200 is input. The first characteristic evaluation model 121 has been machine-learned (deep learning) using the graph data 200 indicating the shape of the rotor 12 and characteristic data of the motor equipped with the rotor 12 as learning data.

[0055] 6(A), graph data 200 output from the generator 110 is given to the input of the first characteristic evaluation model 121. When the graph data 200 is input, the first characteristic evaluation model 121 can output motor characteristics.

[0056] The evaluator 120 shown in FIG. 6(B) includes a converter 125 and a second characteristic evaluation model 126. The converter 125 converts the graph data 200 into an image. The image represents the shape of the rotor 12 indicated by the graph data 200 in a predetermined image data format (e.g., bitmap). The second characteristic evaluation model 126 has been trained by machine learning so that, when an image showing the shape of the rotor 12 is input, the second characteristic evaluation model 126 outputs the characteristics of a motor including a rotor 12 having the shape shown in the image. The second characteristic evaluation model 126 has been trained by machine learning (deep learning) using the image showing the shape of the rotor 12 and characteristic data of a motor including the rotor 12 as training data.

[0057] 6(B), graph data 200 output from generator 110 is provided to converter 125. Converter 125 converts the provided graph data 200 into an image and outputs it. The image output from converter 125 is provided as an input to second characteristic evaluation model 126. When the image is input, second characteristic evaluation model 126 can output motor characteristics.

[0058] The evaluator 120 in FIG. 6(A) does not require the converter 125 of the evaluator 120 in FIG. 6(B), thereby simplifying the configuration. Furthermore, the evaluator 120 in FIG. 6(B) can utilize an image-based evaluation model 126. Image input is common in characteristic evaluation using machine learning models, and many excellent existing techniques for characteristic evaluation exist. Therefore, by adopting an image-input evaluation model 126 as in FIG. 6(B), it is possible to utilize many existing techniques for characteristic evaluation.

[0059] As the motor characteristic evaluation model 126 for image input shown in Fig. 6(B), for example, a prediction model (deep learning model) described in Non-Patent Documents 1 and 2 can be used. As the motor characteristic evaluation model 121 for graph input shown in Fig. 6(A), one can be used that is constructed by machine learning (deep learning) using the input of the prediction model described in Non-Patent Documents 1 and 2 as graph data 200 instead of an image.

[0060] Note that data other than the graph data 200 or images (for example, operating conditions of the motor, other conditions of the motor) may also be input to the evaluation models 121, 126 in FIGS. 6(A) and 6(B).

[0061] An example of a preferred embodiment of the present invention is shown in FIG. 8. FIGS. 8(A-1) and (A-2) each show the same structure, with FIG. 8(A-1) illustrating that the structure is made of materials A, B, and C. FIG. 8(A-2) indicates the vertices of the structure with symbols 1 to 6. FIG. 8(B) shows an adjacency matrix based on FIG. 8(A-1), FIG. 8(C) shows a two-dimensional region vector based on FIG. 8(A), and FIG. 8(D) shows a three-dimensional region vector. By using such preferred region vectors, information processing related to structures and the like can be performed more efficiently. Note that FIGS. 8(C) and (D) show region vectors focusing on the region of the structure. Region vectors focusing on the region of the structure minimize additional parameters in the shape modification operation, thereby more effectively preventing the generation of an inappropriate shape.

[0062] The present invention is not limited to the above-described embodiment and can be modified in various ways. In the above-described embodiment, a motor is described as one suitable example. However, the present invention can be applied to other applications. For example, the present invention can be applied to piezoelectric actuators, RFID (Radio Frequency Identification), cables, high-frequency preheaters, capacitors, magnetic heads, magnetic shields, magnetic gears, magnets, circuit breakers, sensors, electrical heating devices, electrolytic plating, electromagnetic forming, electromagnetic retarders, electromagnetic relays, transformers and reactors, bus bars, generators, heaters, bearings, magnetic couplings, induction heating devices, induction machines, linear solenoids and linear actuators, and wireless power transfer systems. When the present invention is applied to a motor, it has the advantage of being able to express motor design information using only the minimum necessary information. [Industrial Applicability]

[0063] The system and program of the present invention are particularly useful in the design of motors and the like. [Explanation of symbols]

[0064] 10: Interior permanent magnet synchronous motor 11: Stator 12: Rotor 15: Permanent magnet 16: Slot 100: Design System 110: Generator 120: Evaluator 121: Motor characteristic evaluation model 125: Converter 126: Motor characteristic evaluation model 150: Judgment device 200: Graph data 310: Generative Model 320: Property prediction model 400:Image 450: Motor characteristics

Claims

1. A system for designing a second structure based on a first structure, comprising means for designing the second structure using material domain information indicating the domains of each constituent material of the first structure and property information indicating the properties of the first structure.

2. 2. The system according to claim 1, further comprising means for designing said second structure using the required specifications for said second structure.

3. 2. The system of claim 1, wherein said means designs two or more of said second structures having different characteristics.

4. The system of claim 1 , wherein the material domain information includes air domain information.

5. The system of claim 1 , wherein the material domain information includes magnetic material domain information.

6. The system according to claim 1 , wherein the material region information indicates each region for each material property of each constituent material.

7. The system of claim 1 , wherein the characteristic information includes information indicative of a physical characteristic.

8. The system of claim 1 , wherein the structure is an electrical device.

9. 2. The system according to claim 1, further comprising an evaluation unit that, upon receiving the material domain information from a user, evaluates the characteristics of the first structure from the material domain information.

10. The system according to claim 9, wherein the system learns the relationship between the material domain information and the characteristics, predicts the characteristics from the material domain information of the user through the learning, and outputs the predicted evaluation value to the user.

11. A program that causes a system for calculating the characteristics of a structure to function as a means for learning the relationship between the characteristics using material domain information indicating the domains of each constituent material of the structure, predicting the characteristics from the material domain information received from a user through said learning, and outputting the predicted evaluation value to the user.

Images