Abstract: A computer-based method is disclosed for creating and/or editing a feature control frame (FCF) for geometric dimensioning & tolerancing (GD&T) of a model in a computer-aided design (CAD) program. The method includes displaying, in a graphics area of the CAD program, a cell of a FCF for a geometric feature of the model, displaying a context menu adjacent to the cell of the FCF, wherein the context menu comprises a first plurality of user-selectable input options associated with GD&T information for the geometric feature, receiving a user selection of one of the first plurality of user-selectable input options, and subsequently presenting a second plurality of user-selectable input options associated with GD&T information for the geometric feature. The options included in the second plurality of user-selectable input options depend, at least in part, on which of the first plurality of user-selectable input options the user selected.

Abstract: A computer-implemented method for designing a 3D modeled object via user-interaction. The method includes obtaining the 3D modeled object and a machine-learnt decoder. The machine-learnt decoder is a differentiable function taking values in a latent space and outputting values in a 3D modeled object space. The method further includes defining a deformation constraint for a part of the 3D modeled object. The method further comprises determining an optimal vector. The optimal vector minimizes an energy. The energy explores latent vectors. The energy comprises a term which penalizes, for each explored latent vector, non-respect of the deformation constraint by the result of applying the decoder to the explored latent vector. The method further includes applying the decoder to the optimal latent vector. This constitutes an improved method for designing a 3D modeled object via user-interaction.

Abstract: A computer-implemented method for selecting a vertex among vertices of a 3D object in a 3D immersive environment of a CAD system where each vertex has a position in the 3D immersive environment including displaying the 3D object in the 3D immersive environment, detecting a hand gesture including opposing the pads of the index finger and the thumb, both pads being spaced, determining a segment parallel to a segment connecting the pads of the index finger and the thumb, the determined segment having a position in the 3D immersive environment, and identifying the vertex of the 3D object having the closest position with the determined segment.

Abstract: Computer implemented techniques for simulating a fluid flow about a surface of a solid, include receiving a coordinate system for representation of a curvilinear mesh that conforms to the surface of the solid, simulating, with a lattice velocity set transport of particles in a volume of fluid, with the transport causing collision among the particles, executing a distribution function for transport of the particles, with the distribution function including a particle collision determination and a change in particle distribution associated with the curvilinear mesh, performing by the computing system, advection operations in the coordinate system under constraints applied to particle momentum values and mapping by the computer system values resulting from simulating onto the curvilinear mesh by translation of the particle momentum values and spatial coordinates determined in the coordinate system into momentum and spatial values in the curvilinear space.

Abstract: Disclosed are techniques for simulating a physical process and for determining boundary conditions for a specific energy dissipation rate of a k-Omega turbulence fluid flow model of a fluid flow, by computing from a cell center distance and fluid flow variables a value of the specific energy dissipation rate for a turbulent flow that is valid for a viscous layer, buffer layer, and logarithmic region of a boundary defined in the simulation space. The value is determined by applying a buffer layer correction factor as a first boundary condition for the energy dissipation rate and by applying a viscous sublayer correction factor as a second boundary condition for the energy dissipation rate.

Abstract: A computer-implemented method of machine-learning. The method comprises providing a dataset of 3D modeled objects each representing a mechanical part. Each 3D modeled object comprises a specification of a geometry of the mechanical part. The method further comprises learning a set of parameterization vectors each respective to a respective 3D modeled object of the dataset and a neural network configured to take as input a parameterization vector and to output a representation of a 3D modeled object usable in a differentiable simulation-based shape optimization. The learning comprises minimizing a loss that penalizes, for each 3D modeled object of the dataset, a disparity between the output of the neural network for an input parameterization vector respective to the 3D modeled object and a representation of the 3D modeled object. The representation of the 3D modeled object is usable in a differentiable simulation-based shape optimization.

Abstract: Systems, methods, and computer program products can be used for determining the amount of oil removed by a miscible gas flood. One of the methods includes identifying locations of oil within a volume representing a reservoir rock sample. The method includes identifying locations of gas within the volume. The method also includes determining the amount of oil removed based on locations within the volume where oil is either coincident with the gas or is connected to the gas by a continuous oil path.

Abstract: A computed-implemented method for processing a computer-aided design 3D model of a mechanical part including a portion having a distribution of material. The method including obtaining the 3D model, the 3D model including a skin portion of the 3D model representing an outer surface of the portion of the mechanical part. The method further including processing the skin portion based on an extrusion-processing algorithm, where a transform of the skin portion is inputted to the algorithm. The transform represents an unfolding of the distribution of material of the portion.

Abstract: A computer-implemented method for parametrization of a computer-aided design 3D model of a mechanical part including a portion having a distribution of material arranged as a sweep. The sweep has a trajectory and a boundary. The method includes obtaining the 3D model, the 3D model including a skin portion representing an outer surface of the portion of the mechanical part, and one or more vector fields, each vector field representing the boundary and/or the trajectory. The method further includes, for each vector field, determining a distribution of values of a respective parameter of the skin portion by optimizing an objective function which rewards alignment of a gradient of a candidate parameter with the vector field.

Abstract: A computer-implemented method for material extrusion detection in a portion of a mechanical part having a distribution of material. The method includes obtaining a CAD 3D model of the mechanical part including a skin portion representing an outer surface of the portion of the mechanical part and an extrusion axis. The method further includes computing a ratio of an area of an orthogonal projection of the skin portion over an area of the skin portion. The method further includes determining whether or not the distribution of material is arranged as an extrusion based on the ratio and a ratio threshold. The outer surface is determined to be an extrusion surface when the ratio is lower than the ratio threshold. This forms an improved solution for processing a CAD 3D model of a mechanical part.

Abstract: A computer-implemented method for forming a dataset configured for learning a neural network. The neural network is configured for inference, from a freehand drawing representing a 3D shape, of a solid CAD feature representing the 3D shape. The method includes generating one or more solid CAD feature includes each representing a respective 3D shape. The method also includes, for each solid CAD feature, determining one or more respective freehand drawings each representing the respective 3D shape, and inserting in the dataset, one or more training samples. Each training sample includes the solid CAD feature and a respective freehand drawing. The method forms an improved solution for inference, from a freehand drawing representing a 3D shape, of a 3D modeled object representing the 3D shape.

Abstract: A method includes identifying machine process parameters for an additive manufacturing process to produce a part, providing a real-world sensor to sense a characteristic associated with a real-world version of the additive manufacturing process, receiving sensor readings from the real-world sensor while the machine is performing the real-world version of the additive manufacturing process, generating, with a computer-based processor, physics-based features associated with the additive manufacturing process, and training a machine-learning software model based at least in part on the machine process parameters, the sensor readings, and the physics-based features to predict a behavior of the real-world sensor.

Abstract: A computer-implemented method for automatically providing at least a template of a furnished virtual 3D room including 3D elements, including, for each furnished virtual 3D room: automatically extracting a spatial relations graph based on spatial relations between the 3D elements of the 3D room, said 3D elements including 3D architectural elements and 3D furnishing objects located in the furnished virtual 3D room, automatically extracting at least one zone from the 3D room based on the spatial relations graph, and extracting a set of constraints about a relative arrangement of said zone with respect to the room architecture or with respect to other zones of the 3D room, a zone being defined by a local spatial arrangement of at least one 3D furnishing object, and storing a template of the furnished virtual 3D room, said template including said zone and said set of constraints.

Abstract: A method in a computer aided drafting application for replicating a component mating in a modeled assembly includes examining constraints and geometry surrounding a selected component of the component mating in a first surface of the assembly. A first descriptor with a plurality of numerical characteristics of the constraints and geometry is captured. The first descriptor is set as a first seed descriptor. A potential first target geometry in the region of the first face is examined and a first target descriptor is computed according to the first target geometry. If first seed descriptor matches the first target descriptor, an instance of a first target component is created according to the first target descriptor.

Abstract: A computer-implemented method for learning an autoencoder notably is provided. The method includes obtaining a dataset of images. Each image includes a respective object representation. The method also includes learning the autoencoder based on the dataset. The learning includes minimization of a reconstruction loss. The reconstruction loss includes a term that penalizes a distance for each respective image. The penalized distance is between the result of applying the autoencoder to the respective image and the set of results of applying at least part of a group of transformations to the object representation of the respective image. Such a method provides an improved solution to learn an autoencoder.

Abstract: Systems, methods, and computer program products can be used for visualizing the behavior of flow of two or more fluid phases, wherein a fluid phase behavior is represented in a visualization. One of the methods includes determining an occupation time, which is the amount of elapsed time from when a fluid phase first occupies a particular location until a second time. The method includes generating data for a visualization, with a location in the visualization corresponding to the particular location, and with the generated data for that location in the visualization indicating the occupation time.

Abstract: The disclosure notably relates to a computer-implemented method for instancing a global physics simulation. The method includes obtaining a set of local simulations. The set of local simulations includes at least one local simulation. A local simulation is a physics simulation that is part of the global physics simulation and that can be computed alone and independently of the global physics simulation. Each local simulation of the set of local simulations is already computed. The method further includes, for each local simulation of the set of local simulations, computing a respective reduced model of the local simulation. The method further includes computing each global simulation of a set of at least one global simulation. Each global simulation is an instance of the global physics simulation. This constitutes an improved method for instancing a physics simulation.

Abstract: A computer-based system and method generate a movement of a 3D part of a 3D assembly in a 3D scene. Movement of the 3D part represents rotation by a predetermined angle. The method a) displays a 3D assembly of 3D parts in the 3D scene; b) selects a 3D part of the 3D assembly; and c) displays in the 3D scene a 3D manipulator. The 3D manipulator comprises three axes, and is anchored to the 3D part at an anchor point. The method then d) drags the 3D manipulator along one axis by a current distance from the anchor point on the axis. The predetermined angle corresponds to a maximum distance from the anchor point. The method e) while dragging the 3D manipulator, computes a ratio of the current distance to the maximum distance, and f) generates the movement of the 3D part proportionally to the ratio.

Abstract: A computer-implemented method of machine-learning including obtaining a dataset of training samples. Each training sample includes a pair of 3D modeled object portions labelled with a respective value. The respective value indicates whether or not the two portions belong to a same segment of a 3D modeled object. The method further includes learning a neural network based on the dataset. The neural network takes as input two portions of a 3D modeled object representing a mechanical part and outputs a respective value. The respective value indicates an extent to which the two portions belong to a same segment of the 3D modeled object. The neural network is thereby usable for 3D segmentation. The method constitutes an improved solution for 3D segmentation.

Abstract: A computer-implemented method for machine-learning a function configured, based on input covariates representing medical characteristics of a patient with respect to a multi-state model of an illness having states and transitions between the states, to output a distribution of transition-specific probabilities for each interval of a set of intervals, the set of intervals forming a subdivision of a follow-up period. The machine-learning method including obtaining a dataset of covariates and time-to-event data of a set of patients, and training the function based on the dataset. This forms an improved solution for determining accurate patient data with respect to a multi-state model of an illness.