Methods and systems for modeling physical systems may use a hybrid approach for surrogate modeling that incorporates both modeling based on physical principles and fitting to data. For example, a method for developing a reduced-order models (ROM) of a physical system may comprise: defining a quantity of interest (QoI) for the physical system; defining a lumped-parameter surrogate (LPS) of the physical system based on physical principles; deriving a topology from the LPS; deriving a governing equation of the ROM from the topology, wherein the governing equation has unknown parameters; collecting data about the QoI of the physical system; and fitting the governing equation based on the data to derive values for the unknown parameters and yield the ROM, wherein the ROM approximates the QoI.

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.

A battery model construction method includes: a step ST2 for constructing a battery model; steps ST3 and ST4 for evaluating, for each sample battery, the prediction error between a measured value of the SOH and a predicted value according to the battery model, and determining whether there is inherent bias in the prediction error for each sample battery; steps ST5 and ST6 for constructing a first error prediction model associating explanatory variables defined on the basis of usage history parameters with an objective variable, and determining whether a first correlation exists between the measured value of the average prediction error acquired in steps ST3 and ST4 and the predicted value according to the first error prediction model; and a step ST7 for reconstructing the battery model in the case where it is determined that there is bias and that the first correlation exists in steps ST5 and ST6.

A method for coupling hydraulic fracture network extension and production performance of a horizontal well in an unconventional oil and gas reservoir includes: establishing a complex hydraulic fracture network model of a fractured horizontal well in an unconventional oil and gas reservoir based on a fracture extension theory; constructing three-dimensional three-phase mathematical models of seepage for the fractured horizontal well based on an embedded discrete fracture model; and constructing a fully implicit numerical calculation model by a finite difference method through three-dimensional orthogonal grids, and solving iteratively, thereby accurately predicting a production performance characteristic of the fractured horizontal well in the unconventional oil and gas reservoir. The method combines a fracture extension model with a production performance prediction model to realize the coupled simulation and prediction of the hydraulic fracture network extension and production performance of the horizontal well in the unconventional oil and gas reservoir.

A method, a system, an equipment and a medium for modifying the layering layer information of finite element model (FEM) unit. The method includes: obtaining a unit ID to be modified; determining the file to be modified according to the unit ID to be modified; receiving the new layering layer attribute ID regarding to the unit ID to be modified inputted by user; and replacing the original layering layer attribute ID corresponding to the unit ID to be modified in the file to be modified with the new layering layer attribute ID to complete the modifying the layering layer information of FEM unit. The method can directly modify the layering layer information of the units in the model file, without importing the model file into the pre-processing software to modify it and then exporting it again, thereby reducing the workload of technical staff and saving development time.

Modelling of distributed event-discrete systems using digital twins. In more detail, the present disclosure relates to the field of modeling distributed event-discrete systems using digital twins for subsequent use of the models during real time control of distributed even-discrete systems. Heretofore, there are provided a computer implemented meta model, modeling methods using the computer implemented meta model, and related modeling engine and service engine. In the computer implemented meta model at least one state in the at least one state model is represented by a set of partial states using different levels of data abstraction according to a meta description, a range of values characterizing a target for a considered state, and a range of values representing an actual constellation of a considered state.

Disclosed is a method for analyzing a sulfide-based solid electrolyte using computer simulation including connecting, by a user, to a client accessible to a server, inputting information of a sulfide-based solid electrolyte to be analyzed to the client, transmitting, by the client, the information to the server, implementing, by the server, generation of a three-dimensional structure in which anion clusters and lithium ions are disposed, based on the transmitted information, feeding back, by the server, an implementation result to the client, and displaying, by the client, the feedback result. In addition, properties of sulfide-based solid electrolytes, which cannot be observed by experimentation, can be analyzed based on lithium, ion conductivity.

This application relates to a method for analyzing the dynamic characteristics of carbon composite materials. In the method, a specimen for a specific carbon fiber orientation is prepared and a modal test is performed on the specimen to obtain data and analyze the characteristics of the specimen on the basis of the data. To solve conventional carbon composite material dynamic characteristic analysis methods for carbon composite materials, the proposed method can determine the orientation of carbon fiber orientation exhibiting desired dynamic characteristics by predicting various system parameters at the state of designing, i.e., before the manufacture of a carbon composite material. Especially, the method predicts system parameters such as structural stiffness and viscous damping coefficient which are very sensitive to the orientation of carbon fiber, using only data of a single reference orientation, and reflects the prediction results on the design of a carbon composite material.

Methods, systems, and computer programs encoded on computer storage media, for selecting a model out of a number of models based on subject characteristics. One of the methods includes obtaining present subject connectivity matrix data for a present subject, obtaining present subject data describing the present subject where the present subject data is different from the present subject connectivity matrix data, determining a specific model to apply to the present subject connectivity matrix data based at least in part on the present subject data, determining the specific model using a model trained with fMRI data for brains of a plurality of past subjects and past subject data describing the past subjects, applying the specific model to identify a potential present subject brain condition based at least in part on the present subject connectivity matrix data, and taking an action based on identification of a potential present subject brain condition.

Methods, systems, and apparatus, including medium-encoded computer program products, for designing three dimensional lattice structures include, in one aspect, a method including: obtaining a mechanical problem definition including a 3D model of an object; generating a numerical simulation model for the 3D model of the object using one or more loading cases and one or more isotropic solid materials identified as a baseline material model for a design space; predicting performance of different lattice settings in different orientations in the design space using a lattice structural behavior model in place of the baseline material model in the numerical simulation model; and presenting a set of lattice proposals for the design space based on the predicted performance of the different lattice settings in the different orientations; wherein the lattice structural behavior model has been precomputed for the different lattice settings, which are generable by the 3D modeling program.