Adaptive Tuning Physics-Based Digital Twins
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Solution Overview
Problem
Physics-based digital twins face discrepancies between simulated and real-world outputs due to inaccuracies in physics-based model parameters, which are computationally expensive to determine and update, especially in real-time environments with limited resources.
Innovation Solution
A technique using adaptive filtering to iteratively update filter coefficients and map them to physics-based parameter sets, enabling efficient on-the-fly parameter tuning that closely matches real-world data, leveraging a trained mapping between filter coefficients and physics-based parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optimization methods are used to determine physics-based parameters, then parameter accuracy is improved, but computational cost and time increase significantly
Solution Approach 1:
The patent introduces an intermediary mapping function that translates complex physics-based parameter optimization into a simpler filter coefficient optimization problem. This mapping function acts as a mediator between the physics model parameters and the adaptive filter coefficients, allowing efficient real-time tuning while maintaining accuracy.
Solution Approach 2:
The patent changes the parameter representation from direct physics-based parameters to filter coefficients that can be updated using adaptive filtering. This parameter transformation enables real-time optimization by converting the complex nonlinear optimization problem into a simpler iterative coefficient adjustment problem.
2Adaptability or versatility
If physics-based parameters are updated in real-time, then adaptability to real-world systems is improved, but computational resources required increase
Solution Approach 1:
The patent creates a simplified copy of the physics model in the form of an adaptive filter that mirrors the essential dynamics. This filter copy can be updated efficiently using standard adaptive filtering algorithms, providing real-time adaptability without requiring computationally intensive reoptimization of the full physics model.
Solution Approach 2:
The patent implements dynamic parameter updating through adaptive filtering, where filter coefficients are continuously adjusted based on real-time error signals. This dynamic adjustment mechanism enables the system to adapt to changing real-world conditions while maintaining manageable computational requirements through iterative coefficient updates rather than full model reoptimization.
3Measurement precision
If complex optimization algorithms are applied to tune physics-based parameters, then parameter accuracy is improved, but device complexity increases
Solution Approach 1:
The mapping function serves as an intermediary that simplifies the optimization process by translating complex physics-based parameter adjustments into simpler filter coefficient updates. This intermediary layer reduces the complexity of the optimization algorithm while maintaining the accuracy needed for reliable digital twin parameter tuning.
Data Source
AI summary
A computer-implemented method is disclosed for automatically tuning a digital twin of a physical system (302) that utilizes a physics-based model (304) of the physical system (302). The method uses a trained mapping (308) between a physics-based parameter set of the physics-based model (304) and a filter coefficient set of an adaptive filter (306) applied to the physical system (302). An adaptive filtering-based approach is used to update the filter coefficient set at discrete time steps based on an error between an output signal measured from the physical system (302) in response to an input signal and an output response computed by the physics-based model (304) for the same input signal. The trained mapping (308) is then used to determine updated parameter values in the physics-based parameter set from the updated filter coefficient set. The method may be used to adapt a digital twin simulation at runtime to closely match the behavior of a physical system (302).