Physics-Informed Neural Equalization for Nonlinear Data Access
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Solution Overview
Problem
Existing data communications and storage systems face challenges in signal waveform equalization due to hardware defects, channel noise, and signal processing imperfections, with model-based methods being numerically intractable, prone to model mismatch, and requiring high-computing resources, while data-driven methods lack interpretability and require large training datasets.
Innovation Solution
Integrating physics-informed neural networks (PINNs) that merge physical models with DNNs to adaptively train parameters, using models like nonlinear Schrödinger equation, Volterra expansion, and digital-twin based designs to mitigate signal distortions, enabling efficient and interpretable signal compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data-driven learning-based methods using overparameterized DNNs are used, then the ability to approximate functions with precision is improved, but the requirement for massive high-quality training data and computing resources increases
Solution Approach 1:
The patent introduces physics-informed neural networks that use physical models (Maxwell equations, nonlinear Schrödinger equation) as intermediary frameworks to guide the learning process. These physical models act as mediators between the input data and the neural network parameters, enabling the system to achieve high function approximation precision without requiring massive training data by leveraging the underlying physical principles of signal propagation and distortion.
Solution Approach 2:
The patent changes the approach from purely data-driven parameter optimization to physics-constrained parameter learning. By incorporating physical models with known parameters (permittivity, permeability, geometric dimensions) as constraints, the neural network learns fewer parameters while maintaining high precision, thereby reducing the quantity of training data needed while preserving function approximation capability.
2Measurement precision
If data-driven learning-based methods using overparameterized DNNs are used, then the ability to approximate functions with precision is improved, but the computing power and resources required increase
Solution Approach 1:
The patent uses physical models as intermediary frameworks that reduce the search space for optimal parameters. By constraining the neural network to operate within physically plausible parameter ranges defined by Maxwell equations and nonlinear Schrödinger equation, the computing resources required for training are significantly reduced while maintaining high function approximation precision.
Solution Approach 2:
The patent transforms the optimization problem by changing from unconstrained parameter space to physically-constrained parameter space. This reduces the dimensionality of the optimization problem and enables efficient training with reduced computing power requirements while preserving the ability to approximate complex signal distortion functions with high precision.
3Reliability
If data-driven learning-based methods are used, then signal distortion compensation is achieved, but the interpretability and certifiability of the trained model decrease
Solution Approach 1:
The patent introduces physical models as interpretable intermediary frameworks that bridge the gap between black-box neural network predictions and physical understanding. The physical models (Maxwell equations, nonlinear Schrödinger equation) provide explicit mathematical relationships that can be interpreted and verified, while the neural network learns to optimize parameters within these physical frameworks, thereby maintaining both compensation reliability and model interpretability.
Solution Approach 2:
The patent changes the nature of model parameters from purely empirical fitting parameters to physically-constrained parameters with known physical meanings (permittivity, permeability, geometric dimensions). This transformation enables the model to maintain interpretability while achieving reliable signal distortion compensation, as the parameters can be traced back to physical properties of the communication channel.
4Productivity
If model-based methods are used, then signal processing is performed, but the complexity of building accurate signal models increases
Solution Approach 1:
The patent merges the strengths of both model-based and data-driven approaches by combining physical models with neural networks. The physical models provide structured constraints that simplify the learning process, while the neural networks handle the complexity of real-world signal distortions. This hybrid approach reduces the overall complexity compared to building accurate signal models from scratch, as the physical models provide a foundational framework that guides the learning process.
Solution Approach 2:
The patent changes the approach to model building by using physically-constrained parameter spaces. Instead of attempting to model all possible signal distortions with complex mathematical frameworks, the system uses known physical parameters (from Maxwell equations and nonlinear Schrödinger equation) as constraints, thereby simplifying the model building process while maintaining signal processing efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The PINN approach reduces redundant parameters, improves model accuracy, and lowers computational complexity, providing reliable data transfer with reduced training data requirements and enhanced interpretability.
Implementation Method 1
physics-informed neural networks that model physical phenomena like the nonlinear Schrödinger equation and Kerr nonlinearity
Implementation Method 2
physics-informed neural networks that model physical phenomena like the nonlinear Schrödinger equation and Kerr nonlinearity
Data Source
AI summary
A system and a computer-implemented method using physics-informed neural network (PINN) are provided for data communications. At a transmitter, the method is configured to acquire source data to be transmitted, encode the source data to codewords based on forward error correction (FEC) codes, map the codewords to amplitude symbols, modify the mapped amplitude symbols to pre-equalized symbols including pre-distort channel impairments symbols based on a predetermined physical model, and transmit digital data of the pre-equalized symbols over a channel as channel data. At a receiver, the method is configured to receive and demodulate the channel data from the channel to produce an initial estimate of bits of the received channel data, mitigate channel noise and waveform distortions in the channel data based on the initial estimate, convert the channel data consisting of shaped non-uniform symbols into a deshaped bit sequence as a uniform data sequence, decode the deshaped bit sequence to correct residual errors in the converted channel data, and store the corrected channel data to a data sink.