arXiv Machine Learning August 12, 2026 By Haiteng Wang, Yunfei Zhu, Tao Wang, Yikang Li, Jiabao Dong, Xiaoge Zhang, Lei Ren

Physics-informed Diffusion Generative Model for Time-Series Data Synthesis in Dynamic Systems

Read the original on arXiv Machine Learning →

arXiv:2608. 10941v1 Announce Type: new Abstract: Industrial time-series signals, such as turbine temperature and rotational speed in aero-engines, are essential for monitoring the health and operational status of complex dynamical systems.

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

diffusion

arXiv Machine Learning

Jun 3

Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing

arXiv:2604. 07366v2 Announce Type: replace Abstract: Partial differential equations (PDEs) govern nearly every physical process in science and engineering, but solving them at scale remains prohibitively expensive.

By Yilong Dai, Shengyu Chen, Xiaowei Jia, Runlong Yu

diffusion safety

arXiv AI

Jun 11

Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling

arXiv:2606. 11605v1 Announce Type: cross Abstract: Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models.

By Ge Song, Kiarash Naghavi Khanghah, Anandkumar Patel, Rajiv Malhotra, Hongyi Xu

llms efficiency safety

arXiv Machine Learning

5d ago

Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations

arXiv:2602. 12706v2 Announce Type: replace Abstract: Neural operators have emerged as fast surrogate solvers for parametric partial differential equations (PDEs).

By Heechang Kim, Qianying Cao, Hyomin Shin, Seungchul Lee, George Em Karniadakis, Minseok Choi

rag diffusion benchmarks

arXiv Machine Learning

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Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics

arXiv:2607. 09582v1 Announce Type: cross Abstract: We present a physics-constrained machine learning framework for accelerating the direct numerical simulation (DNS) of turbulent reacting flows.

By Okezzi Ukorigho, Opeoluwa Owoyele

arXiv AI

Jun 30

Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations

arXiv:2601. 13534v3 Announce Type: replace-cross Abstract: Time series generation (TSG) is widely used across domains, yet most existing methods assume regular sampling and fixed output resolutions.

By Xu Zhang, Junwei Deng, Chang Xu, Hao Li, Jiang Bian

diffusion

arXiv Machine Learning

Jun 4

Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing

arXiv:2606. 04582v1 Announce Type: cross Abstract: Real-time monitoring of the temperature distribution within components and sub-structures is a challenging topic in many systems due to restrictions on feasible sensor locations.

By Monika Stipsitz, H\`elios Sanchis-Alepuz, Jacob Reynvaan, Silvester Sabathiel

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