Systems, methods, and other embodiments described herein relate to altering an image and propagating changes to other images of the same object using a diffusion model. In one embodiment, a method includes acquiring object images depicting an object. The method includes, responsive to altering one of the object images into an edited image, adapting the object images to reflect changes in the edited image by iteratively applying a diffusion model to the object images until satisfying a consistency threshold. The method includes providing the object images to represent an edited version of the object.

A model representing a physical object in a shape optimization according to a design objective, and a control point for altering a shape of the physical object are received. The shape is defined by a set of nodes in the model. Sibling models are generated from the model according to a perturbation scheme. The control point is perturbed with respective perturbed values for the sibling models. Each sibling model contains nodal location changes for the nodes. The nodal location changes are determined based on a respective shape function formulated according to a respective perturbed value at the control point and one or more simulated physical behaviors of the model. The model is updated to have an optimal value for the control point. The optimal value is identified from a relationship according to the design objective. The relationship correlates a physical characteristic of the sibling models to the respective perturbed values.

Tools and techniques are described to attach a device to a controller, whereby the controller analyzes the device inputs, looks up information about the device in a database, and then determines which inputs on the device match the defined device inputs. It then may translate information received from the device into an intermediate language. It may also use the information received from the device, the location of the device, and information about the device to create a digital twin of the device.

In various embodiments, a training application trains a machine learning model to modify portions of shapes when designing 3D objects. The training application converts first structural analysis data having a first resolution to first coarse structural analysis data having a second resolution that is lower than the first resolution. Subsequently, the training application generates one or more training sets based on a first shape, the first coarse structural analysis data, and a second shape that is derived from the first shape. Each training set is associated with a different portion of the first shape. The training application then performs one or more machine learning operations on the machine learning model using the training set(s) to generate a trained machine learning model. The trained machine learning model modifies at least a portion of a shape having the first resolution based on coarse structural analysis data having the second resolution.

A system and method generates a compensation circuit element for an optical circuit design by receiving an optical circuit design. The optical circuit design includes optical circuit elements and channels optically connecting the optical circuit elements. Further, a first compensation length for a first channel of the channels is determined based on a first measured length parameter of the first channel and a first design length parameter associated with the first channel. A compensation circuit element is determined based on the first compensation length. An updated optical circuit design is determined based on the compensation circuit element.

The process comprises positioning wells one after another in a group of potential cells of a geocellular model, each positioning of a well comprises: calculating for each cell of the group of potential cells, a fluid property insertion point driver (DFP1) representative of a fluid property maximization; calculating for each cell of the group of potential cells, a maximized distance insertion point driver (DMD1) representative of a maximization of a distance to another cell or group of cells having at least an undesired property; calculating for each cell of the group of potential cells a combined insertion point driver based on the fluid property insertion point driver (DFP1) and the maximized distance insertion point driver (DMD1); defining a well insertion point of the well being positioned at the cell having a maximal combined insertion point driver.

A method for optimizing a layout of a tensor memory defines at least one hard constraint for allocating a plurality of input/output (I/O) vectors for reading and writing data for a task in the tensor memory. The at least one hard constraint is applied to determine one or more potential conflicts between the plurality of I/O vectors. One or more soft constraints aimed at mitigating the one or more potential conflicts between the I/O vectors may also be generated. The at least one hard constraint is applied in a maximum satisfiability (MaxSAT) solver. The one or more soft constraints may also be applied in the MaxSAT solver. The MaxSAT solver determines locations of the data in the tensor memory. The starting addresses of the input data to be read and of output data to be written by each of the I/O vectors are updated in the tensor memory.

A design engine is configured to automatically generate designs for multi-family housing projects that simultaneously meet local construction regulations while also meeting specific financial targets. A design generator within the design engine generates a first generation of design options that reflect historical design trends. A design evaluator within the design engine then generates design metrics that quantify various attributes of the different design options. The design generator identifies a subset of the design options that optimally balance some or all of the various design metrics, and then generates a subsequent generation of design options that includes design features derived from the subset of design options.

A design engine automatically generates designs for multi-family housing projects that simultaneously meet local construction regulations while also meeting specific financial targets. The design engine includes a design analyzer, a site analyzer, a design generator, and a design evaluator. The design analyzer generates design trends based on a historical database of designs. The site analyzer generates design criteria based on relevant construction regulations. The design generator generates design options that reflect the design trends while also adhering to the construction regulations. The design evaluator then analyzes the design options and generates various design metrics. Based on the design metrics, the design generator generates additional design options that better meet the design criteria.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design with feature thickness control, include: a three-dimensional modeling program configured to provide voxelized thinning including preparing a voxelized sheet and line skeleton for a three-dimensional shape of a three-dimensional model, and/or ramped scaling based thickness constraint application during shape and/or topology generation including modifying an amount of change indicated by a current numerical assessment generated by numerical simulation. The three-dimensional modeling program can be an architecture, engineering and/or construction program (e.g., building information management program), a product design and/or manufacturing program (e.g., a CAM program), and/or a media and/or entertainment production program (e.g., an animation production program).