System and method for multi-model generative simulation modeling of complex adaptive systems
AssigneeFractal Industries, Inc.
InventorsJason Crabtree, Andrew Sellers
Life of this patent
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Abstract
A system and method for multi-model generative simulation modeling of complex adaptive systems, comprising a generative simulation platform, a multidimension time series datastore, and a directed computational graph, capable of running a multitude of simulations with complex and shifting model data, and an optimization engine which can introduce changes into a simulation to represent unforeseen or random changes and events to introduce changes and shifts in the simulation that might not otherwise occur.
Claims10
A system for multi-model generative simulation modeling of complex adaptive systems, comprising:
- a computer system comprising at least a memory, a processor, and an operating system;
- a generative simulation platform comprising at least a first plurality of programming instructions, wherein the plurality of programming instructions, when operating on the computer system, cause the computer system to:
- receive some combination of object, environment, or simulation data from a resource over a network;
- parse received data using pattern recognition;
- parametrize parsed data into objects for model building;
- and
- alter parameters or objects to simulate random or unknown events occurring;
- a multidimensional time series datastore comprising at least a second plurality of programming instructions, wherein the plurality of programming instructions, when operating on the computer system, cause the computer system to:
- create a first dataset by retrieving from memory previously gathered and analyzed data based at least in part on a plurality of perils;
- and
- create a second dataset by retrieving from memory synthetically generated data based at least on the plurality of perils;
- and
- a directed computational graph comprising at least a third plurality of programming instructions, wherein the plurality of programming instructions, when operating on the computer system, cause the computer system to:
- retrieve the first and second datasets from the time series data retrieval and storage server;
- and
- comparatively analyze the first dataset against second dataset to determine an optimal model to use for predictive simulation.
A method for multi-model generative simulation modeling of complex adaptive systems, comprising the steps of:
- receiving some combination of object, environment, or simulation data from a resource over a network, using a generative simulation platform;
- parsing received data using pattern recognition, using a generative simulation platform;
- parametrizing parsed data into objects for model building, using a generative simulation platform;
- altering parameters or objects to simulate random or unknown events occurring, using a generative simulation platform;
- creating a first dataset by retrieving from memory previously gathered and analyzed data based at least in part on a plurality of perils, using a multidimensional time series datastore;
- and
- creating a second dataset by retrieving from memory synthetically generated data based at least on the plurality of perils, using a multidimensional time series datastore;
- and
- retrieving the first and second datasets from the time series data retrieval and storage server, using a directed computational graph;
- comparatively analyzing the first dataset against second dataset to determine an optimal model to use for predictive simulation, using a directed computational graph.
Dependencies are inferred from the claim text.