Reduced-Variable Input-Response Modeling for Unique Parameter Mapping
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Solution Overview
Problem
Current mechanistic computer models for predicting input-response relationships in complex systems, such as drug development and environmental simulations, are costly, insufficient, and ambiguous due to insufficient characterization of heterogeneous systems, nonlinearities, and lack of one-to-one relationships between model parameters and outputs.
Innovation Solution
A non-mechanistic, differential-equation-free approach using a non-compartmental method to predict time-dependent responses, employing a formula that includes scaling and exponential parameters to model complex systems accurately, 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 predictions can be made for complex systems, but the models become complex, insufficient, and ambiguous due to heterogeneous systems and nonlinearities
Solution Approach 1:
The patent replaces traditional mechanistic computer models with a data-driven machine learning approach. Instead of using complex differential equations and mechanistic frameworks to model input-response relationships, the invention uses trained neural networks that learn patterns directly from experimental data, thereby reducing model complexity while maintaining or improving prediction accuracy for heterogeneous and nonlinear systems
Solution Approach 2:
The patent transforms the modeling approach by changing from fixed mechanistic parameters to adaptive data-driven parameters. The machine learning model learns optimal parameters from training data, allowing the system to adapt to heterogeneous conditions and nonlinear behaviors without requiring complex a priori mechanistic assumptions
2Reliability
If physical modeling is used to characterize and predict input-response relationships, then accurate predictions can be obtained, but the costs become very expensive
Solution Approach 1:
The patent creates a virtual copy of the physical system through machine learning models. Instead of conducting expensive physical experiments and clinical trials to characterize input-response relationships, the invention trains computational models on available data to generate accurate predictions, thereby reducing the need for costly physical testing while maintaining prediction reliability
Solution Approach 2:
The patent substitutes expensive physical modeling and experimentation with computationally efficient machine learning approaches. The data-driven models capture system behavior without requiring costly physical measurements and experiments, significantly reducing research and development costs while maintaining accurate prediction capabilities
3Loss of energy
If mechanistic models are used to avoid costs of physical modeling, then costs are reduced, but the models are insufficient and ambiguous due to lack of one-to-one relationships between parameters and outputs
Solution Approach 1:
The patent replaces mechanistic models with data-driven machine learning models that directly map inputs to outputs through learned patterns. This substitution eliminates the ambiguous intermediate parameter transformations in mechanistic models, providing clearer and more direct input-output relationships while maintaining cost efficiency
Data Source
AI summary
Non-mechanistic, differential-equation-free approaches are provided for predicting a particular structure-activity response of a system using a reduced scale and a reduced number of variables. 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.