# Interpretable neural network system identification method for two families of second-order systems based on characteristic curves

arXiv: https://arxiv.org/abs/2509.10632 · HTML: https://mlsanity.com/paper/2509.10632/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 87 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control](https://mlsanity.com/paper/1611.03537/) (2016)
- [Interpretable Scientific Discovery with Symbolic Regression: A Review](https://mlsanity.com/paper/2211.10873/) (2022)
- [Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data](https://mlsanity.com/paper/2009.08810/) (2020)
- [Deep networks for system identification: a Survey](https://mlsanity.com/paper/2301.12832/) (2023)
- [Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE](https://mlsanity.com/paper/2012.06691/) (2020)
- [Free-Form Variational Inference for Gaussian Process State-Space Models](https://mlsanity.com/paper/2302.09921/) (2023)
- [Adaptive parameters identification for nonlinear dynamics using deep permutation invariant networks](https://mlsanity.com/paper/2501.11350/) (2025)

## Built on by (most cited first, 2 of 2)

- [Data-driven forced response analysis with min-max representations of nonlinear restoring forces](https://mlsanity.com/paper/2603.16746/) (2026)
- [PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability](https://mlsanity.com/paper/2606.05191/) (2026)

## Abstract (arXiv)

Nonlinear system identification often involves a fundamental trade-off between interpretability and flexibility, often requiring the incorporation of physical constraints. We propose a unified data-driven framework that combines the mathematical structure of the governing differential equations with the flexibility of neural networks (NNs). At the core of our approach is the concept of characteristic curves (CCs), which represent individual nonlinear functions (e.g., friction and restoring components) of the system. Each CC is modeled by a dedicated NN, enabling a modular and interpretable representation of the system equation. To demonstrate the versatility of the CC-based formalism, we introduce three identification strategies: (1) SINDy-CC, which extends the sparse regression approach of SINDy by incorporating the mathematical structure of the governing equations as constraints; (2) Poly-CC, which represents each CC using high-degree polynomials; and (3) NN-CC, which uses NNs without requiring prior assumptions about basis functions. Our results show that all three approaches are well-suited for systems with simple polynomial nonlinearities, such as the van der Pol oscillator. In contrast, NN-CC demonstrates superior performance in modeling systems with complex nonlinearities and discontinuities, such as those observed in stick-slip systems. The key contribution of this work is to demonstrate that the CC-based framework, particularly the NN-CC approach, can capture complex nonlinearities while maintaining interpretability through the explicit representation of the CCs. This balance makes it well-suited for modeling systems with discontinuities and complex nonlinearities that are challenging to assess using traditional polynomial or sparse regression methods, providing a powerful tool for nonlinear system identification.