Differentiable Physical Model Inferring Device

The patent transforms the non-differentiable model into a differentiable model by changing the mathematical parameters and structure. This allows the model to maintain its ability to represent complex physical phenomena while enabling accurate calculation of parameter sensitivities through differentiation. The differentiable model uses continuous and differentiable functions to describe physical relationships, allowing gradient-based optimization and sensitivity analysis.

The patent creates a hybrid model that combines the strengths of both approaches: it uses a differentiable physical model structure that conforms to physical laws while incorporating neural network elements for learning complex patterns. The model serves multiple functions - it maintains physical consistency through its structure-based formulation while simultaneously capturing complex non-linear relationships through learned parameters, thus achieving both physical law conformity and accurate parameter sensitivity calculation.

The patent replaces traditional numerical differentiation methods with an analytical differentiation approach based on the differentiable model structure. Instead of using complex numerical approximation techniques or black-box neural networks, the model uses closed-form derivatives that can be efficiently computed. This substitution simplifies the overall system while maintaining high accuracy in parameter inference.