A method of creating a fabricable segmented design for a physical device is provided. A computing system receives a design specification. The computing system optimizes an initial segmented design based on the design specification to create an improved segmented design. The computing system perturbs the improved segmented design to create a perturbed segmented design. The computing system optimizes the perturbed segmented design to create a second improved segmented design.

The present disclosure belongs to the field of petroleum engineering, and specifically relates to a subsea Christmas tree re-prediction method integrating Kalman filter and Bayesian network. The subsea Christmas tree re-prediction method integrating Kalman filter and Bayesian network includes three steps: digital twin model establishment, degradation process re-prediction model establishment, and remaining useful life calculation model establishment. The subsea Christmas tree re-prediction system integrating Kalman filter and Bayesian network includes a subsea distribution unit information acquisition subsystem mounted on an subsea distribution unit, a subsea control module information acquisition subsystem mounted on a subsea control module, a subsea valve bank information acquisition subsystem mounted on a subsea valve bank, a wellhead mechanical module information acquisition subsystem mounted on a wellhead mechanical module, a subsea environmental information acquisition module mounted on a subsea control module, and a subsea Christmas tree digital twin subsystem mounted in an overwater control station.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include 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 being loaded with stacks of pallets comprising the preloaded stacks and unloaded stacks. The acts also can include determining positions of empty floor spots remaining in the trailer. The acts 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. The acts further can include determining a second portion of the incremental load design. The acts additionally can include updating the incremental load design based on an overall load design of the trailer. The acts further can include outputting at least the incremental load design, as updated. The incremental load design can specify a respective floor spot assignment for each of the unloaded stacks. Other embodiments are described.

A virtual data acquisition component generates a building performance model having a plurality of predicted building performance metrics. A physical data acquisition component obtains a plurality of trended building performance metrics. An integrated interface receives the building performance model from the virtual data acquisition component having the plurality of predicted building performance metrics and the plurality of physical building performance metrics from the physical data acquisition component. The integrated interface enables the comparison of the predicted building performance consumption metrics with the trended building performance metrics to identify performance gaps.

The disclosed embodiments include reservoir simulation systems and methods to dynamically improve performance of reservoir simulations. The method includes obtaining input variables for generating a reservoir simulation of a reservoir, and generating the reservoir simulation based on the input variables. The method also includes determining a variance of computation time for processing the reservoir simulation. In response to a determination that the variance of computation time is less than or equal to a threshold, the method includes performing a first sequence of Bayesian Optimizations of at least one of internal and external parameters that control the reservoir simulation to improve performance of the reservoir simulation. In response to a determination that the variance of computation time is greater than the threshold, the method includes performing a second sequence of Bayesian Optimizations of at least one of the internal and external parameters.

The present disclosure provides systems and methods for controllable protein generation. According to some embodiments, the systems and methods leverage neural network models and techniques that have been developed for other fields, in particular, natural language processing (NLP). In some embodiments, the systems and methods use or employ models implemented with transformer architectures developed for language modeling and apply the same to generative modeling for protein engineering.

Various examples are provided related to automated chip design, such as a pareto-optimization framework for automated network-on-chip design. In one example, a method for network-on-chip (NoC) design includes determining network performance for a defined NoC configuration comprising a plurality of n routers interconnected through a plurality of intermediate links; comparing the network performance of the defined NoC configuration to at least one performance objective; and determining, in response to the comparison, a revised NoC configuration based upon iterative optimization of the at least one performance objective through adjustment of link allocation between the plurality of n routers. In another example, a method comprises determining a revised NoC configuration based upon iterative optimization of at least one performance objective through adjustment of a first number of routers to obtain a second number of routers and through adjustment of link allocation between the second number of routers.

The present disclosure relates to a method for determination of a real subsoil geological formation. In at least one embodiment, the method includes receiving a model representing the real subsoil, determining a first stochastic trajectory of a first fluvial formation in said model, defining a constraint zone comprising said first fluvial formation, and determining a second stochastic trajectory of a second fluvial formation in said model within said constraint zone based on a stochastic process.

Method and system are disclosed for characterizing and quantifying composite laminate structures. The method and system take a composite laminate of unknown ply stack composition and sequence and determine various information about the individual plies, such as ply stack, orientation, microstructure, and type. The method and system distinguishes between weave types that exhibit similar planar stiffness behaviors, but which produce different failure mechanisms. Individual ply information then is used to derive the laminate bulk properties from externally provided constitutive properties of the fiber and matrix, including extensional stiffness, bending-extension coupling stiffness, bending stiffness, and the like. The laminate bulk properties are then used to generate a probabilistic failure envelope for the composite laminate. This provides the ability to perform non-destructive QA to ensure that individual lamina layup was accomplished according to specifications, and results are used to identify numerous laminate properties beyond purely structural ones.

Systems and methods for molecular simulation in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting a relationship between a ligand and a receptor. The method includes steps for identifying a plurality of conformations of a receptor, computing docking scores for each of the plurality of conformations and a set of one or more ligands, and predicting a relationship between the set of one or more ligands and the plurality of conformations of the receptor.