In an example, a method includes receiving, at a processor, object model data representing at least a portion of an object to be generated by an additive manufacturing apparatus by fusing build material. Using a processor and from the object model data, a property diffusion model for the object in object generation may be determined. Using a processor and based on the property diffusion model, a manufacturing boundary object shell around the object and encompassing an external volume may be determined. The shell may have a variable thickness determined so as to include build material for which, in generation of the object, the property modelled in the property diffusion model has a value which is predicted to conform to a predetermined parameter.

This evaluation device comprises: a mathematical model acquisition unit that acquires a mathematical model expressing the state of a power storage element; an operation data acquisition unit that acquires operation data which includes time-series input data input during operation of a system constructed on the basis of the numerical model, and time-series output data output by the system on the basis of the time-series input data; a processing unit that inputs the time-series input data to the numerical model and executes processing causing time-series model output data to be output from the numerical model; and an evaluation unit that evaluates the design and the operation of the system on the basis of the time-series output data and the time-series model output data.

Systems and methods for a compact wall model are provided. According to one aspect, embodiments herein provide a method that comprises receiving input data related to an enclosure, the input data including solar intensity data on an exterior of a wall of the enclosure, generating, by a processor, a thermal model of a wall of the enclosure based at least in part on the input data, the wall modeled as having a plurality of layers and the thermal model including a plurality of nodes such that each layer of the plurality of layers is associated with at least one node of the plurality of nodes and each node of the plurality of nodes is thermally coupled to an adjacent node by a thermal resistance, solving, by the processor, an energy balance equation for the at least one node to determine a predicted temperature for the at least one node, and output the predicted temperature to a display device.

A method is disclosed for generating a component mesh of a component that may be built-up layer by layer in an additive manufacturing build-up process. The method includes providing a three-dimensional initial component mesh composed of initial mesh elements of uniform shape which include initial mesh nodes and initial mesh edges extending between the initial mesh nodes; slicing the initial component mesh by at least one cutting plane such that initial mesh elements are divided into at least two resulting mesh elements, wherein at the intersection points of the at least one cutting plane with edges of initial mesh elements resulting mesh nodes are defined; determining the position of each initial mesh element with respect to each cutting plane and thus which initial mesh element is divided into resulting mesh elements and which is not; and determining the shape of each resulting mesh element.

The invention relates to general technology for monitoring the state of a battery, e.g., a lithium-ion battery. A thermal simulation model is used for this purpose. Different examples relate to the parameterizing of the thermal simulation model.

An estimation method is performed by a computer for estimating a far field of electromagnetic waves or heat. The method includes: generating an emphasis pattern image obtained by emphasizing each target element of a pattern image of a target circuit by an emphasizing method that corresponds to a type of each target element, with respect to the target element which is at least a part of elements included in the target circuit; and estimating the far field of electromagnetic waves or heat radiated from the target circuit by an existing estimation model using the emphasis pattern image.

A structure is provided that reduces the stress generated in a semiconductor device package during cooling subsequent to solder reflow operations for coupling semiconductor devices to a printed circuit board (PCB). Stress reduction is provided by coupling solder lands to metal-layer structures using traces on the PCB that are oriented approximately perpendicular to lines from an expansion neutral point associated with the package. In many cases, especially where the distribution of solder lands of the semiconductor device package are uniform, the expansion neutral point is in the center of the semiconductor device package. PCB traces having such an orientation experience reduced stress due to thermal-induced expansion and contraction as compared to traces having an orientation along a line to the expansion neutral point.

To generate a deep learning model, basic training data corresponding to a combination of device data and simulation result data is generated using a compact model that generates the simulation result data indicating characteristics of a semiconductor device corresponding to the device data by performing simulation based on the device data. A deep learning model is trained based on the basic training data such that the deep learning model outputs prediction data indicating the characteristics of the semiconductor device and uncertainty data indicating uncertainty of the prediction data. The deep learning model is retrained based on the uncertainty data. The deep learning model may precisely predict the characteristics of the semiconductor device by training the deep learning model to output the prediction data and the uncertainty data and retraining the deep learning model based on the uncertainty data.

According to an embodiment, an information processing device of an embodiment includes a memory and one or more processors coupled to the memory. The memory stores therein time-series data including at least one of a dependent variable and an independent variable. The one or more processors are configured to: generate a nonlinear function based on at least one of the dependent variable and the independent variable; generate a linear regression equation in which the nonlinear function is a basis function; estimate a coefficient of the linear regression equation; calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correct the coefficient based on the degree of influence; and output the linear regression equation expressed by the corrected coefficient.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing thermal modeling. In one aspect, a method includes receiving monitoring data comprising temperature data measured inside a site, mode data, and state data, receiving weather data descriptive of weather at the site, and aligning the received temperature data, mode data, and state data with the received weather data. The method also includes determining an internal heat gain representing an amount of heat generated at the site irrespective of the heating or cooling system, determining at least one of a thermal product for the site or a thermal potential for the heating or cooling system, generating, based on the internal gain and the thermal product or the thermal potential, a thermal model for the site, and providing, as output, the generated thermal model.