arXiv Machine Learning August 14, 2026 By Tianshuo Zhang, Xianglei Xing, Wenzhe Zhai, Jia Gao, He Cao

History-informed Lagrangian Neural Networks

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arXiv:2608. 13215v1 Announce Type: new Abstract: Forecasting the long-horizon evolution of mechanical systems from position-only observations is a pivotal yet difficult task, as hidden velocities and trajectory-specific physical properties must be inferred simultaneously.

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benchmarks

arXiv Machine Learning

Jun 2

Physics-Guided Recurrent State-Space Neural Networks for Multi-Step Prediction

arXiv:2606. 02278v1 Announce Type: cross Abstract: State-space models are traditionally based on physical knowledge, but multi-step predictions from these physical models can be poor due to model inaccuracy.

By Ruiyuan Li, Ajay Seth, Manon Kok

robotics

arXiv Machine Learning

Jul 17

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

arXiv:2607. 15180v1 Announce Type: new 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.

By Ahmet Demirkaya, Georgios Stratis, Tales Imbiriba, Zachary D. Danziger, Deniz Erdogmus

benchmarks

arXiv Machine Learning

Aug 13

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

Jul 7

MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning

arXiv:2605. 13305v2 Announce Type: replace Abstract: Neural ordinary differential equations (Neural ODEs) often fit training trajectories while generalizing poorly to unseen initial conditions and long horizons.

By Lake Yang, Antonio Malpica-Morales, Frank Ioannis Papadakis Wood, Serafim Kalliadasis

benchmarks

arXiv Machine Learning

Sep 21

Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting

arXiv:2601.21151v3 Announce Type: replace Abstract: Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection,...

By Carlos A. Pereira, St\'ephane Gaudreault, Valentin Dallerit, Christopher Subich, Shoyon Panday, Siqi Wei, Sasa Zhang, Siddharth Rout, Eldad Haber, Raymond J. Spiteri, David Millard

diffusion benchmarks

arXiv Machine Learning

Sep 17

LSR-Net: Learning the Forward Evolution Operator for Nonlinear Fluid Dynamics

arXiv:2609. 19039v1 Announce Type: cross Abstract: We introduce the Long-Short-Range Neural Network (LSR-Net), a novel neural operator architecture designed for data-driven forward evolution modeling, and extends it to the prediction of nonlinear fluid dynamics.

By Qian Hou, Sutrisno, Yuqing Li, Zecheng Gan

benchmarks

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Physics-Guided Recurrent State-Space Neural Networks for Multi-Step Prediction

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