Systems, methods, and non-transitory computer-readable storage media for a fast power network simulator. A system configured per this disclosure can use identify a power network, the power network comprising generators, transmission lines, and loads, and receive a model of the power network. The model of the power network can include: models of the generators modeled as differential equations, and models of the transmission lines and the loads modeled as algebraic equations. The system can convert, via a processor, the algebraic equations of the models of the transmission lines and the loads to additional differential equations, then combine, via the processor, the differential equations and the additional differential equations, to yield combined differential equations. The system can then iteratively solve linear equations, via the processor, associated with the combined differential equations, to yield solutions, and output the solutions as part of a power simulation of the power network.

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 modal superposition method using a response dependent non-linear mode concept for a vibration analysis of non-linear engineering structures is provided. The modal superposition method is provided to find steady state response of non-linear systems in frequency domain. The modal superposition method is used in many mechanical structures, especially in design of aerospace and automotive structures, defense industry platforms, steam and gas turbines and mechanical structures containing non-linear forces such as gas turbine engines and jet engines.

Systems, methods, and computer readable media are described to train a compressed neural network with high robustness. The neural network is first adversarially pre-trained with both original data as well as data perturbed by adversarial attacks for some epochs, then “unimportant” weights or filters are pruned through criteria based on their magnitudes or other method (e.g., Taylor approximation of the loss function), and the pruned neural network is retrained with both clean and perturbed data for more epochs.

Systems, methods, and computer readable media are described to train a compressed neural network with high robustness. The neural network is first adversarially pre-trained with both original data as well as data perturbed by adversarial attacks for some epochs, then “unimportant” weights or filters are pruned through criteria based on their magnitudes or other method (e.g., Taylor approximation of the loss function), and the pruned neural network is retrained with both clean and perturbed data for more epochs.

Disclosed is a multiagent system with agents in communication with each other via a communication network. The agents have heterogeneous time-invariant dynamics such that all of the agents have a primary set of synchronization roles that are different from a secondary set of synchronization roles of a subset of the agents.

A non-transitory recording medium records a program that causes a computer to execute a process of causing a display to display, in response to one or more input operations accepted via an input device, one first mathematical expression display area including one first mathematical expression, one first graph display area associated with the one first mathematical expression display area, one first slider display area associated with the one first graph display area, one second mathematical expression display area including one second mathematical expression, one second graph display area which is associated with the one second mathematical expression display area and is an area different from the one first graph display area, and one second slider display area which is associated with the one second graph display area and is an area different from the one first slider display area.

A non-transitory recording medium records a program that causes a computer to execute a process of causing a display to display, in response to one or more input operations accepted via an input device, one first mathematical expression display area including one first mathematical expression, one first graph display area associated with the one first mathematical expression display area, one first slider display area associated with the one first graph display area, one second mathematical expression display area including one second mathematical expression, one second graph display area which is associated with the one second mathematical expression display area and is an area different from the one first graph display area, and one second slider display area which is associated with the one second graph display area and is an area different from the one first slider display area.

A computer-implemented numerical simulation method for studying a physical system governed by at least one differential equation such as a fluid in motion. The simulation is launched, making it possible to define a simulation domain. In the computation step, a machine learning algorithm is implemented to predict a global solution to the equation in the simulation domain. The computation step includes n consecutive sequences, each sequence includes cutting a piece in the simulation domain followed by predicting a local solution in the piece on the basis of local boundary conditions, n being an integer strictly greater than 1. The prediction step being carried out by a machine learning model, as input, global boundary conditions on the simulation domain.

The present disclosure describes a computer implemented method, a system, and a computer program product of determining intrinsic viscosity and Huggins constant of an unknown sample. In an embodiment, the method, system, and computer program product include receiving concentration detector signal values over time from a concentration detector corresponding to a series of aliquots of an unknown sample injected into an instrument chain, receiving specific viscosity values over time from a viscometer corresponding to the series of aliquots, calculating a total mass of each of the aliquots, calculating a first intermediate viscosity value of each of the aliquots, calculating a second intermediate viscosity value of each of the aliquots, and fitting the total mass, the first intermediate viscosity value, and the second intermediate viscosity value to a fitting, resulting in a calculated intrinsic viscosity of the unknown sample and a calculated Huggins constant of the unknown sample.