Embodiments provide mobile computing devices, related systems, and methods for controlling predictive modeling processes on a mobile device. An embodiment may provide a method for controlling predictive modeling processes on a mobile device, which may include receiving modeling data associated with a predictive model, receiving computation resources data of the mobile device, determining one or more predictive model processing parameters of the predictive model based on the modeling data and the computation resources data, formulating processing instructions based on the one or more predictive model processing parameters, and sending the modeling data and the processing instructions to the mobile device for execution of the predictive model. The parameters may include aspects of a sufficient number of tests, a type of predictive modeling simulation technique, client/server processing, and/or device responsive rendering.

Systems and methods for multi-material mesh generation from fill-fraction voxel model data are discussed. Voxel representations of model data are used to generate robust and accurate multi-material meshes. More particularly, a mesh generation pipeline in a virtual fabrication environment is described that robustly generates high-quality triangle surface and tetrahedral volume meshes from multi-material fill-fraction voxel data. Multi-material topology is accurately captured while preserving characteristic feature edges of the model.

A method is disclosed for augmenting the SBR model used in EM field simulation by modeling the specular coherent and diffuse incoherent components of the field scattered by rough surfaces using statistical characteristics of surface roughness. For each projected ray-tube footprint, the magnitude of the coherent radiated field is attenuated with a scalar factor, while the incoherent radiated field is modulated by a random magnitude and phase. Both corrections are based on the statistical characteristics of surface roughness. Multiplying the incoherent field with a randomly generated phase renders it in a mathematically coherent form, which allows the method to generate a statistically viable instance of the total (i.e. coherent plus incoherent) field scattered by a rough surface. The results reproduce the field and power statistics (i.e. mean and variance) observed from direct SBR simulations using an ensemble of explicitly rendered rough surface geometry models, with a significant reduction in computation.

A method including a) receiving a computer generated data set of sequencing constraints describing a software system to be executed on an automation system and including software components and runnable function entities distributed over the number of computing nodes; b) generating a transition matrix from the data set of sequencing constraints, the transition matrix having a plurality of matrix elements each of them describing, by a transition value, a transition from a runnable function entity to another runnable function entity; c) receiving a computer generated communication matrix describing communication links between the computing nodes in the automation system; d) generating a Markov chain out of the data set of sequencing constraints and the communication matrix; e) generating a distribution function from the Markov chain describing used resources of the computing nodes by the software components and runnable function entities; and f) optimizing the allocation of resources.

Embodiments of a method and system for inversion of high-resolution aquifer storage coefficient based on gravity satellite data are disclosed. The method comprises: acquiring gravity satellite data, soil moisture storage data, surface water storage data and other non-groundwater component data, based on a preset region. The method also comprises: obtaining monthly time series of terrestrial water storage anomalies, by inversing the gravity satellite data; obtaining monthly time series of soil moisture storage anomalies, surface water storage anomalies, and other non-groundwater component anomalies; obtaining monthly time series of groundwater storage anomalies; and obtaining monthly time series of groundwater level anomalies in different aquifers. The method yet comprises: obtaining random sample values of storage coefficient by a stochastic simulation; obtaining correlation coefficient and Nash-Sutcliffe efficiency coefficient between random value of groundwater storage trend after a forward modeling and groundwater storage trend in the gravity satellite time-variable signal; and obtaining an inversion result.

A method, according to some implementations, includes obtaining trial design simulation results for a set of trial designs and determining a set of Pareto designs in the set of trial designs based at least in part on the trial design simulation results and one or more performance parameters. The method further includes determining a set of convex hull designs in the set of trial designs, determining a set of recommended designs based at least in part on the set of Pareto designs and the set of convex hull designs, and transmitting the set of recommended designs.

A method for determining predicted failure rates of drug injection devices includes receiving a set of parameters specifying physical properties of a syringe, a liquid drug, and a drug injection device configured to deliver the liquid drug to a patient via the syringe, the drug injection device including a mechanism that drives a plunger rod toward a plunger of the syringe encased in a syringe carrier. The method also includes receiving failure rate data specifying a measured rate of failure of the drug injection device in response to various peak pressures within the syringe, applying the set of parameters to a kinematic model of the drug injection device to determine a predicted peak pressure within the syringe, determining a probability of failure of the drug injection device using the failure rate data and predicted peak pressure, and providing an indication of the determined probability of failure to an output device.

The information processing apparatus (2000) of the example embodiment 1 includes an acquisition unit (2020), a modeling unit (2040), an output unit (2080). The acquisition unit (2020) acquires a plurality of trajectory data. The trajectory data represents a time-sequence of observed positions of an object. The modeling unit (2040) assigns one of groups for each trajectory data. The modeling unit (2040) generates a generative model for each group. The generative model represents trajectories assigned to the corresponding group by a common time-sequence of velocity transformations. The velocity transformation represents a transformation of velocity of the object from a previous time frame, and is represented using a set of motion primitives defined in common for all groups. The output unit (2060) outputs the generated generative models.

A method including obtaining information about a trailer that has been partially loaded with preloaded stacks in a manner that deviates from an original load design. The trailer is loaded with stacks of pallets including (i) the preloaded stacks that have already been loaded in the trailer and (ii) unloaded stacks that have not yet been loaded into the trailer. The method also can include determining positions of empty floor spots remaining in the trailer. The method additionally can include determining a first portion of an incremental load design for the unloaded stacks using a gap-filling pattern behind an uneven rear edge of the preloaded stacks in the trailer. The method further can include determining a second portion of the incremental load design. The method additionally can include updating the incremental load design based on an overall load design of the trailer using a first simulated annealing using a first neighborhood defined by separate rows within a delivery group that does not include the preloaded stacks. The method further can include outputting at least the incremental load design, as updated, to cause the unloaded stacks to be loaded in the trailer according to the incremental load design while the preloaded stacks remain in the trailer. The incremental load design specifies a respective floor spot assignment for each of the unloaded stacks. Other embodiments are described.

A method for determining trial designs is provided. The method includes obtaining simulation data for a set of trial designs. The simulation data includes performance parameters and performance parameter values associated with each design in the set of designs for a set of criteria; determining an optimality criteria for evaluating the trial designs; searching, within the set of trial designs, for globally optimum designs based on the optimality criteria; and recommending globally optimum designs.