arXiv Machine Learning July 14, 2026 By Yurui Zhang, Ruigang Wang, Ian R. Manchester

Robustly Invertible Nonlinear Dynamics and the BiLipREN: From Inversion-Based Control to Generative Trajectory Modelling

Read the original on arXiv Machine Learning →

arXiv:2607. 10026v1 Announce Type: cross Abstract: This paper proposes a new notion of robust invertibility for nonlinear dynamical systems, and introduces constructive parameterizations of recurrent neural network which are robustly invertible by design.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 3

R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks

arXiv:2504. 01250v2 Announce Type: replace Abstract: This paper presents the Robust Recurrent Deep Network (R2DN), a scalable parameterization of robust recurrent neural networks for machine learning and data-driven control.

By Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester

arXiv AI

Jul 2

FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification

arXiv:2511. 10841v3 Announce Type: replace-cross Abstract: Modeling continuous-time dynamics from sparse and irregularly-sampled time series remains a fundamental challenge.

By YongKyung Oh, Dong-Young Lim, Sungil Kim

benchmarks safety

arXiv AI

Aug 6

Beyond Linear Dynamics: Neural Bilinear Dynamical Models for Time Series Forecasting

arXiv:2608. 04471v1 Announce Type: cross Abstract: Time series in real-world applications are often generated by nonlinear dynamical systems, making accurate forecasting challenging.

By Mengzhou Gao, Huangqian Yu, Pengfei Jiao

diffusion

arXiv Machine Learning

6d ago

Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates

arXiv:2608. 11435v1 Announce Type: new Abstract: Forward and inverse modeling of parametric dynamical systems requires surrogate models that are not only accurate for state prediction, but also informative for parameter calibration.

By Qiyao Zhou, Xujia Zhu, Pierre Joli, Yu Cong, Sibo Cheng

benchmarks

arXiv Machine Learning

Jun 4

When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks

arXiv:2606. 04476v1 Announce Type: new Abstract: In this paper, we study the gradient descent dynamics for jointly training both layers of a one-hidden-layer ReLU network to fit a linear target function.

By Berk Tinaz, Changzhi Xie, Mahdi Soltanolkotabi

safety

arXiv Machine Learning

Jun 9

Solving Inverse Problems with Flow-based Models via Model Predictive Control

arXiv:2601. 23231v2 Announce Type: replace-cross Abstract: Flow-based generative models provide strong unconditional priors for inverse problems, but guiding their dynamics for conditional generation remains challenging.

By George Webber, Alexander Denker, Riccardo Barbano, Andrew J Reader

benchmarks

Related stories

R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks

FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification

Beyond Linear Dynamics: Neural Bilinear Dynamical Models for Time Series Forecasting

Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates

When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks

Solving Inverse Problems with Flow-based Models via Model Predictive Control