Patent Description:
It is often desired to optimize a geometry of a physical structure, e.g., when designing such a physical structure using a Computer-Aided Design (CAD) process.

For example, when designing an electrical machine, such as an electrical motor or an electrical generator, it may be desirable to optimize the geometry of the rotor of the electrical machine with respect to one or more objectives.

The optimization with respect to such objectives may involve evaluating one or more performance metrics to obtain one or more quantitative measures based on which the geometry of the physical structure may be optimized. For example, such a performance metric may provide a numerical approximation of a physical quantity or a quantity which is derived from one or more physical quantities. The optimization with respect to multiple of such objectives simultaneously is also known as multi-objective optimization.

Methods for optimizing a geometry of a physical structure are known per se. For example, in sizing optimization, a parametric geometry template may be used to establish variations in the geometry of a physical structure, e.g., by varying the scalar geometry parameters of the parametric geometry template, and to evaluate different variants of the geometry, for example using the Finite Element Method (FEM). The objectives of the FEM simulations may then be used in an optimization algorithm whose goal is to identify an optimal design of the geometry in as few attempts as possible. For example, genetic algorithms or gradient-based algorithms may be used as optimization algorithm.

A drawback of such known types of optimizations is that they may be computationally expensive, e.g., requiring computation times in the range of weeks or months. A reason for this is that known optimization algorithms are typically are unable to use prior knowledge, e.g., from past optimizations, to accelerate a current optimizatior Document <NPL>, discloses the use of CNNs for mechanical stress field prediction in cantilevered structures. Document <NPL>, discloses the optimization of composite plates using genetic algorithms and neural networks. Document <NPL>, discloses discusses a solution approach for predicting the damage state of a rotor disc in an electrical machine using finite element analysis and damage modeling.

It would be desirable to enable an optimization of the geometry of a physical structure to take prior knowledge into account. The following measures provide a neural network which may be trained to incorporate such prior knowledge, and which may be used during an optimization of the geometry of a physical structure to infer values of performance metrics, thereby greatly reducing the computational complexity of the optimization compared to having to compute such performance metric values via FEM or similar methods.

In accordance with a first aspect of the invention, a computer-implemented method and a system are provided for training a neural network for use in optimizing a geometry of a physical structure with respect to at least one performance metric, as defined by claim <NUM> and <NUM>, respectively. In accordance with a further aspect of the invention, a computer-implemented method and a system are provided for optimizing a geometry of a physical structure with respect to at least one performance metric, as defined by claim <NUM> and <NUM>, respectively. In accordance with a further aspect of the invention, a computer-readable medium is provided as defined by claim <NUM>.

The above measures provide a neural network which is trained using training data which comprises a plurality of training instances representing different geometries of the physical structure and associated values of at least one performance metric. The training instances may represent prior knowledge of the relationship between the geometry of the physical structure and the performance metric, and may be obtained from past optimizations and/or from measurements of physical embodiments of the physical structure or elsewhere.

A possible approach to training the neural network may be to consider the scalar geometry parameters from the sizing optimization as input to the neural network, the values of the performance metric(s) as a desired output of the neural network, and to use a machine learning regression method to learn the relationship between input and output and thereby train the neural network to infer the value of the performance metric(s) based on the scalar geometry parameters of a physical structure of which the geometry is to be optimized.

However, the topology of parts of the physical structure often changes for a new optimization problem, and thereby also the number and/or the type of scalar geometry parameters. This makes it difficult or even impossible to merge the scalar geometry parameters across different optimization problems. Accordingly, the training and subsequent use of a neural network may be limited to optimization problems associated with one parametric geometry template, i.e., to one set of scalar geometry parameters.

To address this problem, the above described aspects of the invention provide measures which learn the relationship between the geometry of the physical structure and the values of the performance metric from a pictorial representation of the geometry. Here, the adjective 'pictorial' refers to an image-based representation, such as a representation as one or more 2D raster ('bitmap') images comprised of pixels, or a 3D volumetric image comprised of voxels. Such a pictorial representation is more flexible than a set of scalar geometry parameters, as it may represent any geometrical variation which is discernible in the pictorial representation, compared to a set of scalar geometry parameters which may be limited to representing only those geometric variations which are representable by the set of scalar geometry parameters.

The neural network may be trained using such pictorial representations, and which may be obtained in various ways, e.g., by converting a geometric representation, such as a CAD model or the aforementioned parametric geometry template, into a pictorial representation, or by scanning an actual prototype of the physical structure, etc..

Having trained the neural network, the trained neural network may be used during optimization of a geometry of a same type of physical structure to infer a value of the at least one performance metric in a computationally efficient manner. Namely, during the optimization process, a pictorial representation of the current geometry may be generated and used as input to the trained neural network to infer the value(s) of the performance metric(s). Having obtained the inferred value(s), the geometry of the physical structure may then be (further) optimized, e.g., in a manner as known per se, e.g., using a genetic algorithm or a gradient-based optimization. It is noted that during such an optimization process, the geometry of the physical structure may be stored as computer-readable data, e.g., in the form of the aforementioned parametric geometry template, a CAD model, etc..

The above measures may replace the FE method or similar methods, which may otherwise be used to determine values of performance metrics for a geometric representation of a physical structure, by a trained neural network. The inference using a trained neural network is typically much more computationally efficient than the use of the FE method. In addition, the use of a trained neural network which is specifically trained on a pictorial representation of the physical structure provides additional flexibility compared to the use of a neural network which is trained on only scalar geometry parameters. Advantageously, the computation times for optimizing the geometry of a physical structure may be greatly reduced, for example to the order of several milliseconds or microseconds.

Optionally, each training instance further comprises one or more scalar parameters associated with the physical structure represented by the training instance, and the neural network further comprises a concatenation layer for receiving an output of the plurality of convolutional layers and the one or more scalar parameters as input and for providing a concatenated output to the plurality of dense layers. There may be aspects of the physical structure which may play a role in the optimization of the geometry of the physical structure but which may be more efficiently represented by one or more scalar parameters rather than by a pictorial representation, and/or which may not be discernible in a pictorial representation of the physical structure. To enable such parameters to be encoded as prior knowledge in the neural network, and to enable the inference of performance metric values based on such parameters, the neural network may provide an input for the scalar parameters during training and subsequent use, i.e., inference, namely in the form of a concatenation layer which concatenates the output of the convolutional layers with the scalar input parameters and feeds the concatenated output further, e.g., to dense layer(s) of the neural network.

Optionally, the training method further comprises:.

The different geometries of the physical structure on which the training is based may not all be available as a pictorial representation, but some may rather only be available as a geometric representation, e.g., as a parametric geometry template, a CAD model, etc. Accordingly, as an initial step of the training or before the training, the geometric representations may be converted into corresponding pictorial representations.

Optionally, generating the pictorial representations from the geometric representations comprises:.

Several types of pictorial representations have been found to be suitable to be used as input to a neural network, such as 2D images, 3D volumetric images and 3D multiview images. It is noted that a 3D multiview image may be represented by a series of 2D images, as is known per se in the art. Moreover, the dimensionality of the pictorial representation may be the same dimensionality as the geometric representation, e.g., a 3D CAD model may be represented as a 3D volumetric image, but may also be lower, e.g., if a 2D image suffices for training purposes.

The pictorial representation typically comprises pictorial values, e.g., image values in the case of raster or volumetric images, or may be assigned pictorial values, e.g., in the case of point clouds. Such pictorial values may be assigned not only to represent the geometry of the physical structure, e.g., by having pictorial values representing the physical structure (e.g., '<NUM>') which are different from pictorial values representing the surroundings of the physical structure (e.g. '<NUM>' denoting 'air'), but may also be assigned in such a way so as to encode local properties of the physical structure, such as a material. Thereby, different materials of the physical structure may be identified (e.g., '<NUM>' for sheet metal, '<NUM>' for magnet), both for the training instances and for input instances for the inference, which may increase the specificity with which the trained neural network may infer performance metric values.

Optionally, the physical structure is a part of an electrical machine. For example, the physical structure may be a rotor of the electrical machine.

Optionally, the one or more scalar parameters comprise at least one of a group of: a scalar design parameter of the part of the electrical machine, and a characterization of a geometry of another part of the electrical machine.

Non-limiting examples for scalar design parameters of an electrical machine include material properties, such as Young's modulus and magnetic remanence, loads such as current, voltage, speed, and geometric parameters such as an axial length.

An example of a characterization of another part of the electrical machine is the following: the pictorial representation may show a rotor (or a rotor section), as the rotor geometry may vary greatly depending on the application. In contrast, the stator geometry may be less variable across applications, and any variability may rather be expressed by one or a limited number of scalar values. Using one or a limited number of scalar parameters may be more efficient than using a pictorial representation if the variability of the geometry can be sufficiently represented by such scalar parameter(s).

Optionally, the optimization method further comprises iteratively optimizing the geometry of the physical structure by, in an iteration of said iterative optimization, generating the pictorial representation from the optimized geometry of the physical structure which is obtained in a previous iteration of the iterative optimization.

It will be appreciated by those skilled in the art that two or more of the above-mentioned embodiments, implementations, and/or optional aspects of the invention may be combined in any way deemed useful.

Modifications and variations of any system, any computer-implemented method or any computer-readable medium, which correspond to the described modifications and variations of another one of said entities, can be carried out by a person skilled in the art on the basis of the present description.

<FIG> shows a geometry template <NUM> for sizing optimization of an electrical machine with a single-V rotor topology. The geometry template <NUM> may be represented by a set of scalar geometry parameters, some of which are illustrated in <FIG> with reference numerals 20A-<NUM> and which may represent physical quantities such as heights, widths, angles etc. The set of scalar geometry parameters may be predetermined for a particular optimization problem, and the geometry template <NUM> may be stored as structured data containing values for the respective scalar parameters. Such types of geometry templates are known per se, and used during sizing optimization and similar optimization techniques.

<FIG> show different rotor topologies <NUM>-<NUM> for electrical machines, namely in <FIG> a rotor with a single V topology <NUM>, in <FIG> a rotor with a double V topology <NUM> and in <FIG> a rotor with a VC design topology <NUM>. Further indicated are magnets <NUM> and magnet pockets <NUM> in the rotor. <FIG> together illustrate that the topology of the geometry template may change for a new optimization problem, as for each rotor topology <NUM>-<NUM>, a different set of scalar geometry parameters may be relevant, for example containing a different number and/or different types of scalar parameters. This makes it difficult or even impossible to merge the scalar geometry parameters across different optimization problems, which in turn hinders the reuse of prior knowledge obtained from previous optimizations of the same type of physical structure or from elsewhere.

<FIG> illustrate how the use of a pictorial representation of the physical structure may enable a neural network to be trained to learn the relationship between on the one hand the geometry of the physical structure, and in some embodiments also further properties such as a material selection, and on the other hand performance metric values of a physical structure. In general, the physical structure may be a single part but also a compound part, e.g., an assembly of parts. The physical structure may, but does not need to be, part of a physical machine, apparatus or device, and may in some embodiments represent the entire physical machine, apparatus or device.

The following briefly summarizes an embodiment involving the training of a neural network for use in optimizing a rotor of an electrical machine. Since such an electrical machine is typically a symmetric entity with repeating rotor sectors (with an symmetry angle which may correspond to <NUM>°/(number of pole-pairs x <NUM>)) , it may suffice for the geometry of one rotor sector to be optimized, and thus the neural network to be trained on the geometries of individual rotor sectors. Any future or past reference to the optimization of the geometry of a rotor may thus equally refer to the geometry of a rotor sector.

As will also be explained with further reference to <FIG>, the training may involve obtaining pictorial representations of different geometries of a rotor sector, and possibly additional scalar parameters which are associated with the rotor sector. In addition, performance metric values maybe obtained for these geometries, e.g., from previous simulations, e.g., involving the FEM method, or from measurements of physical embodiments (e.g., prototypes or the like) of the electrical machine, etc..

As will also be explained with reference to <FIG>, a convolutional neural network (CNN) may be provided, which may be configured and trained to extract geometric features from the pictorial representations, and possibly combined with additional scalar parameters, to learn the correlation with value(s) of one or more performance metrics, and thereby to learn to predict such performance metric values from pictorial representations of the geometry of the physical structure. As will also be explained with reference to <FIG>, during subsequent use in the optimization of a rotor sector within the context of a different optimization problem, the trained neural network may be used to predict performance metric values based on the geometry of the rotor sector in a computationally efficient manner. In particular, the trained neural network may be used in an optimization loop to replace the FEM method and thereby to accelerate multi-objective optimizations of electrical machines.

<FIG> illustrates the conversion of a 2D geometric representation of a rotor sector into a pictorial representation in the form of a 2D image. Such conversion may take place during or before the training of the neural network, namely to obtain training instances for the training of the neural network, but also during or before the subsequent use of the neural network, i.e., the subsequent inference by the neural network, namely to convert a geometry of a physical structure to be optimized into a data format which can be used as input to the trained neural network. More specifically, <FIG> shows a vector-based representation <NUM> of a rotor sector, which may be obtained from, for example, a CAD model of the rotor sector, and which may be converted (denoted by arrow <NUM>) into a 2D-image based representation <NUM> using known techniques, for example using a same or similar type of technique which exists for rendering and displaying CAD models on a display.

The 2D image is shown to have a limited resolution, e.g., <NUM> pixels along the horizontal dimension <NUM>-x by <NUM> pixels along the vertical dimension <NUM>-y. The spatial resolution of the 2D image, or in general the spatial resolution of the pictorial representation, may be suitably selected to enable geometric variations to be sufficiently perceptible and thereby the trained neural network to be sufficiently accurate in its inference, while at the same time striving for a limited spatial resolution to reduce the computational complexity involved in training and subsequently using the trained neural network for inference.

The 2D image may be a single-channel image, e.g., having only image component containing image values. Such image values may conventionally be interpreted as intensity values, but may in the present case have a different meaning so as to represent the geometry of the physical structure in the 2D image in an efficient manner. In particular, the geometry of the physical structure may be encoded as image values in a predetermined or standardized manner to provide for normalization across the training instances. For example, a particular image value (e.g., '<NUM>') may be assigned for spatial positions in the 2D image which belong to the physical structure, while another image value (e.g., '<NUM>') may be assigned to the surroundings of the physical structure (e.g., '<NUM>' denoting surrounding air). Note that the conversion may also encode further properties of the physical structure in the 2D image. For example, a different image value may be selected for different materials of the physical structure. In a so-called tertiary conversion (or tertiary 'extraction' of geometric features), sheet metal may be assigned the image value '<NUM>' while magnets may be assigned the image value '<NUM>' (while continuing to assign the image value '<NUM>' for air). The 2D image <NUM> represents an example of this tertiary conversion, which can also be seen in the zoomed-in portion <NUM> of the 2D image <NUM> containing the three possible image values '<NUM>', '<NUM>' and '<NUM>'.

It is noted that the bit depth of the image values may be selected based on the encoding scheme. For example, for tertiary conversion, a <NUM>-bit bit depth may suffice, but for practical reasons, also another bit depth may be selected, e.g., the standard <NUM>-bit bit depth. It is noted that the conversion may use any assignment of geometric features to image values, and may for example use a different order than indicated above (e.g., air = '<NUM>', sheet metal = '<NUM>', magnet = '<NUM>'). In general, it would be advantageous to use a standardized assignment during training, e.g., for all training instances, and during subsequent use.

In general, the conversion of a geometric representation of a physical structure into a pictorial representation may use a same or similar technique as is available for displaying the geometric representation on a display, but which may be adapted to obtain an assignment to image values which is most suitable to represent the geometric features of the physical structures, possibly including the type of material. These type of display techniques are also referred to as rendering techniques, and are known per se in the art.

<FIG> illustrates a neural network <NUM> arranged to, during training and subsequent use, receive a pictorial representation as input and to output performance metric values. In the example of <FIG>, the neural network <NUM> is shown to comprise five convolutional layers C1-C5 which receive a 2D image having a resolution and thereby input shape of 136x216 pixels. The convolutional layers C1-C5 may in general each have a plurality of filters of different sizes, e.g., as indicated by the parameters shown with each convolutional layer in <FIG>, associated activation functions (e.g. Exponential Linear Unit, ELU), and may in general include one or more batch normalization layers. The convolutional layers may then be followed by a flattening operation F (or flattening 'layer') and a concatenation layer CC which may concatenate the flattened output of the convolutional layers with scalar input parameters, being in the example of <FIG> seven parameters, i.e., a 7x1 data vector. The concatenation layer is optional and may only need to be used if it is desired to train the neural network further based on scalar parameters associated with the geometry of the physical structure, as is and will be described elsewhere in this specification. The concatenation layer may then be followed by three dense layers D1-D3, each having a plurality of neurons and activation functions and which may include one or more batch normalization layers.

In the example of <FIG>, the neural network <NUM> is arranged to provide an output of <NUM> scalar parameters, which may represent values of performance metrics and which during the training represent the objectives of the training and during the subsequent use the inferred output by the neural network and thereby the target values for which the geometry is to be optimized. Preferably, the values of the performance metrics are normalized during training.

The scalar input may be used for various scalar parameters associated with the physical structure. For example, such scalar parameters may provide an additional characterization of the physical structure shown in the pictorial representation, or a characterization by one or more scalar value of a geometry of another physical structure which is associated with the depicted physical structure. For example, the geometry of the stator of electrical machines is usually very similar and may be represented using a uniform geometric template, and therefore by one or more scalar parameters representing the geometric template. However, the topology of the rotor varies greatly between electrical machines, and may rather be represented by the pictorial representation. Other scalar parameters may be the number of pole pairs, current strength, voltage bearing, etc..

To enable the neural network to correlate inputs and outputs for different geometries and/or topologies, the training instances representing these different geometries and/or topologies may preferably be trained together in a complete but shuffled dataset. Namely, if the training alternates between one type of geometry and/or topology and another type of geometry and/or topology, this may result in a situation where the most recently trained geometries and/or topologies are less easy to learn and may yield poor predictions.

Example (hyper)parameters for the neural network <NUM> may be the following:.

It will be appreciated that depending on the type of physical structure and/or the general class of geometric optimization problems for which the neural network is to be trained, the neural network may have any other suitable architecture involving convolutional and dense layers, and may be trained using any other suitable set of training parameters.

Having trained the neural network, the trained neural network may then be used in the optimization of a geometry of a same type of physical structure. Essentially, the trained neural network may enable the effect of a change in geometry on the performance metric values to be rapidly evaluated, e.g., in the order of several microseconds or milliseconds. This enables various uses. For example, the trained neural network may be used in a 'pre-optimization' to limit the parameter space for a subsequent FEM-based optimization. Another example is that the entire optimization of the geometry of the physical structure may be performed using the neural network, thereby avoiding use of the FEM.

<FIG> shows an example of the latter. In this example, two optimizers OP1, OP2 may optimize different part of a rotor sector, but in general, any number of such optimizers may be used, e.g., to optimize different parts of the geometry of a physical structure. Such optimizers may be known types of optimizers, for example using a genetic algorithm or a gradient-based optimization, and may perform the optimization based on, for example, a set of scalar geometry parameters which each may represent a parametric geometry template, as are known per se from the field of sizing optimization. The output of each optimizer OP1, OP2 (denoted by arrow <NUM>) may be an optimized set of scalar geometry parameters, which may each represent a geometrical representation of the geometry of the rotor sector. These geometrical representations are depicted in <FIG> as a vector-based drawing <NUM>, <NUM>. Each of these geometric representations may be converted (denoted by arrow <NUM>) into a pictorial representation, e.g., in the manner as described with reference to <FIG> and elsewhere. <FIG> shows, by way of example, one pictorial representation in the form of a 2D image <NUM> obtained for the geometric representation <NUM>.

Both pictorial representations may then be used separately as input (denoted by arrow <NUM>) to the neural network <NUM>, being in this example a neural network comprising four convolutional layers C1-C4, a concatenating layer CC and two dense layers D1-D2. The concatenating layer CC may accept scalar parameters SP as input. It is noted that both the pictorial representation and the scalar parameters may be normalized, e.g., in a manner as described elsewhere. The neural network may then produce output <NUM> in the form of inferred performance metric values, e.g., inferred KPI values, which may be unnormalized by a conversion matrix (not explicitly shown in <FIG>) before being fed back to the respective optimizer OP1, OP2 to allow the optimization to proceed on the basis of the inferred performance metric values. This process may be repeated iteratively, in which in each iteration, a newly optimized geometry for the physical structure may be obtained.

<FIG> further relate to the pictorial representation of the geometry of a physical structure. <FIG> shows another example of a 2D pictorial representation of a rotor sector, showing a 2D point cloud <NUM> comprising a point cloud part <NUM> representing metal sheet and a point cloud part <NUM> representing magnets. Such conversion to a 2D point cloud may involve representing a 2D geometric structure as points which may be distributed along the edges of the 2D geometric structure. Such a point cloud may be expressed as a matrix with N (= number of points) rows and p (= number of dimensions) columns, with each cell of the matrix containing a coordinate value of a respective point in the respective dimension. Since such a matrix may be considered to represent unstructured data, it may be preferred to encode the point cloud in a structured data format, for example using methods such as PointNet (http://arxiv. org/abs/<NUM>) or PointNet++ (https://arxiv. org/abs/<NUM>). If it is desired to assign further value(s) to each point, e.g., to assign a material to each point, the data format may have to be extended with one or more further data channels.

<FIG> illustrates the conversion of a 3D geometric representation of a housing of an electrical machine into a pictorial representation in the form of a 3D voxel representation <NUM>. Alternatively, such a 3D geometric representation may be converted into a 3D point cloud, which may involve representing the 3D geometric structure as points which may be distributed along the faces of the 3D geometric structure. Note that a 3D geometric structure may equally be represented by a 3D volumetric image, e.g., by voxelated data as shown in <FIG>, or using several 2D images showing different views of the physical structure and together representing a so-called 3D multiview image of the physical structure.

<FIG> illustrate 2D image representations <NUM>, <NUM> of rotors in which different winding topologies, with and without pitch shortening, are encoded as different image values. Namely, instead or in addition to encoding a material of the physical structure as a pictorial value, also other types of information may be encoded, such as the phase u or v, or in case of three phases, the phases u, v, or w. Such different winding topologies may thus be represented by assigning different pictorial values, e.g., u = '<NUM>', v = '<NUM>', w = '<NUM>'. <FIG> shows such an assignment either without (<FIG>) or with pitch shortening (<FIG>), and shows the different pictorial values in the form of line-patterns to facilitate reproduction.

<FIG> shows a system <NUM> for training a neural network for use in optimizing a geometry of a physical structure with respect to at least one performance metric, which is elsewhere also simply referred to as 'training system'. The system <NUM> may comprise an input interface <NUM> for accessing training data <NUM> comprising training instances as described elsewhere in this specification. As shown in <FIG>, the input interface <NUM> may be a data storage interface <NUM> to a data storage <NUM> which may comprise said data <NUM>. For example, the input interface <NUM> may be a memory interface or a persistent storage interface, e.g., a hard disk or an SSD interface, but also a personal, local or wide area network interface such as a Bluetooth, Zigbee or Wi-Fi interface or an ethernet or fibreoptic interface. The data storage <NUM> may be an internal data storage of the training system <NUM>, but also an external data storage, e.g., a network-accessible data storage.

The system <NUM> may be further configured to access data <NUM> defining a neural network as described elsewhere in this specification. The neural network may initially be considered an 'untrained' neural network in that parameters of the neural network may not yet be trained, or at least not yet trained to what may be considered a sufficient degree.

As also shown in <FIG>, in some embodiments, the training data <NUM> and the neural network data <NUM> may be accessed from a same data storage <NUM> via a same input interface <NUM>. However, in other embodiments both types of data may be accessed from different data storages, e.g., using different sub-interfaces of the input interface <NUM>.

The system <NUM> is further shown to comprise a processor subsystem <NUM> which may be configured to train the neural network based on the training data <NUM> in a manner as described elsewhere in this specification, thereby obtaining a trained neural network. Although not explicitly shown in <FIG>, the training system <NUM> may, via an output interface, output data representing the trained neural network. For example, the neural network data <NUM> defining the 'untrained' neural network may during or after the training be replaced by the neural network data of the trained neural network, in that parameters of the neural network may be adapted to reflect the training on the training data <NUM>. In other embodiments, the trained neural network data may be stored separately from the neural network data <NUM> defining the 'untrained' neural network. It is noted that the input interface <NUM> may also be an output interface, e.g., a so-called input-output ('I/O') interface <NUM>, via which the storing of the trained neural network data on a data storage may take place.

In general, the training system <NUM> may be embodied as, or in, a single device or apparatus, such as a workstation or a server. The server may be an embedded server or a stand-alone server. The device or apparatus may comprise one or more microprocessors which execute appropriate software. For example, the processor subsystem may be embodied by a single Central Processing Unit (CPU), but also by a combination or system of such CPUs and/or other types of processing units. The software may have been downloaded and/or stored in a corresponding memory, e.g., a volatile memory such as RAM or a non-volatile memory such as Flash. Alternatively, the processor subsystem of the system may be implemented in the device or apparatus in the form of programmable logic, e.g., as a Field-Programmable Gate Array (FPGA). In general, each functional unit of the training system <NUM> may be implemented in the form of a circuit. The training system <NUM> may also be implemented in a distributed manner, e.g., involving different devices or apparatuses, such as distributed local or cloud-based servers.

<FIG> shows a system <NUM> for optimizing a geometry of a physical structure with respect to at least one performance metric using a trained neural network, which is elsewhere also simply referred to as 'optimization system'. The system <NUM> may comprise an input interface <NUM> for accessing data defining a trained neural network <NUM> as obtained by the system <NUM> of <FIG>, or in general representing a trained neural network as described elsewhere in this specification. In the example of <FIG>, the neural network data <NUM> is shown to be accessed from a data storage <NUM>. It is noted that the same implementation options may apply to the input interface <NUM> and the data storage <NUM> as previously as described for respectively the input interface <NUM> and the data storage <NUM> of the training system <NUM> of <FIG>, including interface <NUM> being an input-output ('I/O') interface <NUM>.

The optimization system <NUM> is further shown to comprise a processor subsystem <NUM> which may be configured to optimize a geometry of a physical structure using the trained neural network in a manner as described elsewhere in this specification. For that purpose, the data storage <NUM> is shown to comprise data <NUM> representing the geometry of physical structure, e.g., in the form of a parametric geometry template, which may be accessed via the input interface <NUM> and which may be, during or after optimization, adapted to reflect the optimization of the geometry of the physical structure.

As an optional component, the optimization system <NUM> may comprise a user interface subsystem <NUM> which may be configured to, during operation of the system <NUM>, enable a user to interact with the system <NUM>, for example using a graphical user interface, to control the optimization of the geometry of the physical part. For that purpose, the user interface subsystem <NUM> may comprise a user input interface (not separately shown in <FIG>) which may be configured to receive user input data <NUM> from a user input device <NUM> operable by the user. The user input device <NUM> may take various forms, including but not limited to a computer mouse, touch screen, keyboard, microphone, etc. In general, the user input interface may be of a type which corresponds to the type of user input device <NUM>, i.e., it may be a thereto corresponding type of user device interface. The user interface subsystem <NUM> may further comprise a display output interface (not separately shown in <FIG>) for outputting display data <NUM> to a rendering device, such as a display <NUM>.

In general, the optimization system <NUM> may be embodied as, or in, a single device or apparatus, such as a workstation or a server. The server may be an embedded server or a stand-alone server. The device or apparatus may comprise one or more microprocessors which execute appropriate software. For example, the processor subsystem may be embodied by a single Central Processing Unit (CPU), but also by a combination or system of such CPUs and/or other types of processing units. The software may have been downloaded and/or stored in a corresponding memory, e.g., a volatile memory such as RAM or a non-volatile memory such as Flash. Alternatively, the processor subsystem of the system may be implemented in the device or apparatus in the form of programmable logic, e.g., as a Field-Programmable Gate Array (FPGA). In general, each functional unit of the optimization system <NUM> may be implemented in the form of a circuit. The optimization system <NUM> may also be implemented in a distributed manner, e.g., involving different devices or apparatuses, such as distributed local or cloud-based servers.

Any method described in this specification may be implemented on a computer as a computer implemented method, as dedicated hardware, or as a combination of both. As also illustrated in <FIG>, instructions for the computer, e.g., executable code, may be stored on a computer readable medium <NUM>, e.g., in the form of a series <NUM> of machine-readable physical marks and/or as a series of elements having different electrical, e.g., magnetic, or optical properties or values. The executable code may be stored in a transitory or non-transitory manner. Examples of computer readable mediums include memory devices, optical storage devices, integrated circuits, servers, online software, etc. <FIG> shows an optical disc <NUM>. In an alternative embodiment of the computer readable medium <NUM>, the computer readable medium <NUM> may comprise transitory or non-transitory data <NUM> representing a trained neural network as described elsewhere in this specification. In an alternative embodiment of the computer readable medium <NUM>, the computer readable medium <NUM> may comprise transitory or non-transitory data <NUM> representing a geometry of a physical structure as obtained by an optimization described elsewhere in this specification.

Examples, embodiments or optional features, whether indicated as non-limiting or not, are not to be understood as limiting the invention as claimed.

Claim 1:
A computer-implemented method for training a neural network for use in optimizing a geometry of a physical structure with respect to at least one performance metric, wherein the physical structure is a part of an electrical machine, and wherein the at least one performance metric comprises electrical loads associated with the electrical machine, the method comprising:
- providing training data (<NUM>) comprising a plurality of training instances representing different geometries of the physical structure, each training instance comprising:
- a pictorial representation (<NUM>-<NUM>, <NUM>, <NUM>, <NUM>, <NUM>) of a respective geometry of the physical structure, wherein the pictorial representation comprises one or more 2D raster images comprised of pixels or a 3D volumetric image comprised of voxels;
- a value of at least one performance metric which is associated with the respective geometry of the physical structure;
- providing a neural network (<NUM>, <NUM>) comprising a plurality of convolutional layers (C1-C5) for receiving a respective pictorial representation of the training data as input and a plurality of dense layers (D1-D3) for generating an output of the neural network, wherein the output of the neural network comprises the at least one performance metric; and
- training the neural network using the training data to obtain a trained neural network which is arranged to infer a value of the at least one performance metric from a pictorial representation of a geometry of the physical structure.