ArXiv

RTS Smoother-Guided Learning of Physics-Based Neural Differential Models

2026-07-16 · 16:34 UTC ·Ahmet Demirkaya, Georgios Stratis, Tales Imbiriba... ·1 min read
Authors
Ahmet Demirkaya, Georgios Stratis, Tales Imbiriba...
Categories
cs.LG, eess.SY
arXiv
https://arxiv.org/abs/2607.15180v1
PDF
https://arxiv.org/pdf/2607.15180v1

Brief

An RTS-smoother-guided hybrid neural–physics framework learns missing ODE components by alternating latent-state inference (Rauch–Tung–Striebel smoother) and neural-parameter learning (backpropagation on smoothed trajectories). Evaluated on linear, nonlinear, and stiff benchmarks under partial observations, the method retains mechanistic structure and improves latent-state reconstruction and long-horizon prediction. Only the abstract was provided.

Why it matters

Introduces an RTS-smoother-guided hybrid neural–physics ODE learning scheme that alternates between (1) latent-state inference with a Rauch–Tung–Striebel (RTS) smoother treating model parameters as fixed and (2) neural-network parameter updates via backpropagation on the smoothed trajectories; iterations continue until a stopping criterion.

Key details

  • Evaluated on benchmark linear, nonlinear, and stiff dynamical systems under partial state observation; method preserves interpretable mechanistic structure while improving latent-state reconstruction and long‑horizon prediction compared to pure black‑box approaches (quantitative metrics not reported in the abstract).
  • Authored by Ahmet Demirkaya, Georgios Stratis, Tales Imbiriba, Zachary D. Danziger, and Deniz Erdogmus; posted to arXiv 2026-07-16 as arXiv:2607.15180v1 (cs.LG, eess.SY) with PDF available on arXiv.
Source evidence

Abstract

Ordinary differential equations (ODEs) are widely used to model dynamical systems in physics, biology, neuroscience, and physiology, but in many applications some equations of the dynamics are unknown and only a subset of the state variables are measured. We propose a hybrid neural--physics framework in which the known components of the ODE are kept explicit and the missing components are represented by a neural network. The proposed method consists of two stages where we alternate between state and parameter estimation and iterate until a predetermined criterion is met. Specifically, in the first step, we treat the model parameters as being known and we infer the latent states from the available measurements using a Rauch--Tung--Striebel (RTS) smoother. In the second stage, we treat the smoothed trajectories as being known and use them to estimate the neural networks' parameters through backpropagation. We evaluate the method on benchmark systems spanning linear, nonlinear, and stiff dynamics under partial state observation. Across these settings, the proposed method learns missing ODE components from incomplete measurements while exploiting and retaining interpretable mechanistic structure and improving latent-state reconstruction and long-horizon prediction.