Multi-Model Generative Simulation for Adaptive Predictive Modeling
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Solution Overview
Problem
Current simulation systems are unable to adapt to real-world data and constant input streams during simulation execution, and they are limited in running multiple simulations simultaneously or providing automated parameter adjustments to handle unforeseen changes, which affects simulations in financial markets, pathogen spread, and complex engineering problems.
Innovation Solution
A multi-model generative simulation system that employs a generative simulation platform to receive and parse data, create and run multiple models with varying parameters, and analyze relationships between data sets to determine an optimal model for predictive simulation, using a distributed computing network to process data through pipelines and determine epistemic uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single simulation model is used, then the model structure is simple and easy to manage, but the system cannot adapt to unforeseen changes and lacks robustness in handling complex adaptive systems
Solution Approach 1:
The patent divides the simulation system into multiple independent models (base model, alternative models) that can be executed separately. Each model represents different hypotheses or scenarios, allowing the system to explore multiple possibilities simultaneously without requiring complex interconnections between components
Solution Approach 2:
The patent creates a universal simulation framework that can execute multiple different model types through a common interface. The system uses standardized data structures and execution protocols that allow diverse models to be run within the same environment, reducing overall system complexity while maintaining adaptability
2Reliability
If multiple simulations are run simultaneously to explore different scenarios, then the system can handle unforeseen changes and improve predictive accuracy, but the computational resources and processing time increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-defining alternative models and their parameter ranges before execution. The system prepares multiple simulation scenarios in advance, allowing rapid switching between models during runtime without requiring complex real-time model generation or reconstruction
Solution Approach 2:
The patent implements a tiered simulation approach where the base model runs first to establish baseline behavior, and alternative models are executed selectively based on identified uncertainties or critical decision points. This partial execution strategy reduces overall computational burden while maintaining predictive accuracy where it matters most
3Adaptability or versatility
If manual parameter adjustment is used during simulation execution, then the system can respond to unexpected events, but the response time is delayed and requires human intervention
Solution Approach 1:
The patent implements automated feedback mechanisms that continuously monitor simulation outputs and compare them against expected ranges. When deviations indicate unexpected events, the system automatically triggers parameter adjustments in alternative models without human intervention, enabling real-time adaptation while eliminating response delays
Solution Approach 2:
The simulation system performs self-service by autonomously selecting and executing appropriate models based on real-time data conditions. The system automatically determines when to switch between base and alternative models, adjusts parameters as needed, and generates predictions without requiring external human control, achieving both adaptability and speed
Data Source
AI summary
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.