3results about How to "Less freedom" patented technology

This application discloses a low-scaling coarse-grained modeling method and related apparatus based on machine learning, belonging to the field of molecular dynamics simulation technology. The method includes: acquiring full-atom data of the target system; constructing a coarse-grained model to be learned; the potential function form of the coarse-grained model to be learned includes a potential energy expression describing polar correlation interactions, and the coarse-grained model to be learned is a model validated by full-atom data; using the full-atom data as the optimization target, the potential function parameters to be determined in the coarse-grained model to be learned are automatically iteratively optimized using machine learning methods until preset conditions are met, and the optimized coarse-grained model is output. While preserving key electrostatic and dipole physics mechanisms, this method significantly reduces the degrees of freedom of the coarse-grained model and reduces human intervention through a highly automated process, thereby improving the computational efficiency of complex system simulations and enhancing the model's accuracy, stability, and cross-system transferability.

A solid-liquid bundled rocket structure simplified modeling method, comprising: establishing a rocket structure three-dimensional finite element model, the rocket structure three-dimensional finite element model comprising a main structure model and a secondary structure model of a rocket body; for the main structure model of the rocket body, according to the type of the main structure model of the rocket body, simplifying the type of the main structure model of the rocket body into a low-dimensional model; for the secondary structure model of the rocket body, according to the type of the secondary structure model of the rocket body, simplifying the type of the secondary structure model of the rocket body into a particle element model; simplifying each main structure model and each secondary structure model of the rocket body, and connecting the obtained low-dimensional model and particle element model according to the corresponding relationship of the rocket structure three-dimensional finite element model to obtain a rocket structure simplified model.

The present application relates to a kind of heavy load equal material forming robot real-time dynamics digital twin system modeling method, comprising the following steps: S1, only considering the dynamics model of heavy load equal material forming robot of link elastic deformation is established;S2, construct multi-sensor distributed force / position interactive measurement system, arrange grating ruler, encoder, pressure sensor, data fusion is carried out in combination with Kalman filter;S3, PID algorithm is embedded into control system, realizes the closed-loop control of motion trajectory tracking and force feedback;S4, establish the digital twin framework including five dimensions of physical entity, virtual entity, digital data, connection and service.The present application realizes the five-dimensional digital twin system of heavy load equal material forming robot, provides the overall view of robot dynamics through the interaction of physical space and network space, overcomes the problems such as large resource consumption and response delay of high-fidelity simulation.