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案件記録

System and Method for Simulation of Multiple Dynamic Systems Involving Movement Over Time

発明審査中
2閲覧数
2請求項 · 2 独立
§ Ⅰ

案件概要

発明者

Hadley Hovenden Soutter Arnold; Peter Josef Arnold

IPC分類

G6F 30/27G6F 113/8

CPC分類

G6F30/27G6F2113/8

A system and method for simulating multiple dynamic flows involving movement over time, for example, of water and other fluids, air or wind, fire, or the like, is disclosed. The system and method are a visualization and simulation platform designed to create and execute an approach using deep-learning, computer vision, image processing, and artificial intelligence for predicting all manners of dynamic physical motion over time. The visualization and simulation system is configured to quickly model and predict dynamical physical phenomena including, but not limited to, movement of water or air flow or fire in any topography. The visualization and simulation system predicts flooding behavior patterns in “known” geographical domains (regions and/or areas where the deep-learning system has been explicitly trained on) as well as “unknown” geographical domains (regions and/or areas where the deep-learning system has not been previously exposed or trained on) by providing time-dependent two-dimensional hydrodynamic flooding predictions.

原文(中国語)

A system and method for simulating multiple dynamic flows involving movement over time, for example, of water and other fluids, air or wind, fire, or the like, is disclosed. The system and method are a visualization and simulation platform designed to create and execute an approach using deep-learning, computer vision, image processing, and artificial intelligence for predicting all manners of dynamic physical motion over time. The visualization and simulation system is configured to quickly model and predict dynamical physical phenomena including, but not limited to, movement of water or air flow or fire in any topography. The visualization and simulation system predicts flooding behavior patterns in “known” geographical domains (regions and/or areas where the deep-learning system has been explicitly trained on) as well as “unknown” geographical domains (regions and/or areas where the deep-learning system has not been previously exposed or trained on) by providing time-dependent two-dimensional hydrodynamic flooding predictions.

外部リソース