arXiv AI June 30, 2026 By Xu Zhang, Junwei Deng, Chang Xu, Hao Li, Jiang Bian

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.

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diffusion

arXiv Machine Learning

4d ago

Neural Constitutive Learning for Generalized Reaction-Diffusion Systems

arXiv:2609.37113v1 Announce Type: new Abstract: Generalized reaction-diffusion systems encompass diverse transport mechanisms and coupled reaction kinetics. A central question for neural PDE solvers...

By Shang-Ke Chen, Yu-Peng Wang, Shih-Hsuan Hung, Wei-Fang Sun, Chao-Shun Zhan, Simon See, Min-Jhe Lu

diffusion

arXiv Machine Learning

Aug 7

Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations

arXiv:2608. 06107v1 Announce Type: new Abstract: Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fast, differentiable surrogate models.

By Guillaume Couairon, Alexis Jacq, Yu-Han Wu, Renu Singh, Yana Hasson, Quentin Berthet, Romuald Elie

diffusion fine-tuning benchmarks

arXiv Machine Learning

4d ago

Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps

arXiv:2602.12624v2 Announce Type: replace Abstract: Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited by h...

By Sangwoo Jo, Sungjoon Choi

diffusion benchmarks

arXiv Machine Learning

Jun 9

Learning to Solve Generative ODEs Beyond the Linear Span

arXiv:2606. 08672v1 Announce Type: cross Abstract: Diffusion and flow generative models sample by integrating a learned ODE, but high quality still requires many sequential model evaluations.

By Sihyeon Kim, Seunghun Lee, Vikas Singh, Hyunwoo J. Kim

diffusion benchmarks

arXiv Machine Learning

Jun 16

Towards a Unified Generative Model for Scarce Time Series with Domain Experts

arXiv:2606. 15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios.

By Zihao Yao, Qi Zheng, Jiankai Zuo, Yaying Zhang

llms diffusion fine-tuning

arXiv Machine Learning

Jul 28

Autoregressive One-Step Generative Modeling for Dynamical System Forecasting

arXiv:2605. 05540v2 Announce Type: replace Abstract: Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical structure of long trajectories.

By Tianyue Yang, Xiao Xue

diffusion benchmarks

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Neural Constitutive Learning for Generalized Reaction-Diffusion Systems

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Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps

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