Input-Response Modeling With Unique Parameter-Output Mapping
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Solution Overview
Problem
Current mechanistic computer models for predicting input-response relationships in complex systems are costly, insufficient, and ambiguous, failing to accurately characterize heterogeneous systems and lacking one-to-one relationships between model parameters and outputs, leading to complex and time-prohibitive modeling processes.
Innovation Solution
A non-mechanistic, differential-equation-free approach using a non-compartmental method with a predictive formula C(t)=[M00+M01(kernel)]+[M10+M11(kernel)]{1-e-[N10+N11(kernel)]t1+(eK-2)e-[N10+Nn1(kernel)]t}+...+[Mn0+Mn1(kernel)]{1-e-[Nn0+Nn1(kernel)]t1+(eK-2)e-[Nn0+Nn1(kernel)]t} to model time-dependent responses, providing one-to-one relationships between model parameters and outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mechanistic computer models are used to predict input-response relationships, then the modeling framework is established with first principles, but the model complexity increases and accuracy decreases for heterogeneous systems
Solution Approach 1:
The patent replaces traditional mechanistic modeling approaches (based on differential equations and first principles) with a data-driven non-mechanistic approach using artificial neural networks. This substitution eliminates the need for complex mechanistic frameworks while achieving superior predictive accuracy for heterogeneous systems, directly resolving the contradiction between model complexity and reliability.
Solution Approach 2:
The patent transforms the modeling paradigm by changing from fixed mechanistic parameters to adaptive data-driven parameters. The neural network learns optimal parameters directly from experimental data, allowing the model to adapt to heterogeneous systems without requiring complex a priori mechanistic assumptions, thereby improving accuracy while reducing model complexity.
2Measurement precision
If mechanistic models with multiple parameters are used, then the modeling framework is comprehensive, but one-to-one relationships between parameters and outputs are lost leading to ambiguity
Solution Approach 1:
The patent replaces the mechanistic parameter-based modeling system with a neural network system where the direct mapping from inputs to outputs is learned from data. This substitution preserves the complete information flow without the parameter aggregation and loss that occurs in traditional mechanistic models, eliminating ambiguity while maintaining comprehensive modeling capability.
3Productivity
If traditional computer modeling is used to reduce research costs, then computational simulations can be performed, but the modeling process becomes time-prohibitive for complex systems
Solution Approach 1:
The patent applies preliminary action by training the neural network model on comprehensive experimental data in advance. Once trained, the model can rapidly predict responses to new inputs without requiring time-consuming mechanistic calculations or additional experiments, significantly improving research efficiency while reducing modeling time for complex systems.
Solution Approach 2:
The patent creates a computational copy of the complex system behavior through the trained neural network. This copy captures the essential input-response relationships without requiring the full computational complexity of the original system, enabling rapid predictions that preserve accuracy while reducing modeling time.
Data Source
AI summary
Non-mechanistic, differential-equation-free approaches for unambiguously predicting a particular structure-activity responses of a system to a given molecular structure input are provided in the form of systems, methods, and devices. These approaches provide one-to-one relationships between model parameters and model output to obtain a specificity of results between model parameters and model output to get a unique input-response relationship. The systems, methods, and devices (i) reduce the cost of research and development by offering an accurate modeling of heterogeneous and complex physical systems; (ii) reduce the cost of creating such systems and methods by simplifying the modeling process; (iii) accurately capture and model inherent nonlinearities in cases where sufficient knowledge does not exist to a priori build a model and its parameters; and, (iv) provide one-to-one relationships between model parameters and model outputs, addressing the problem of the ambiguities inherent in the current, state-of-the-art systems and methods.