arXiv Machine Learning July 24, 2026 By Mahindra Rautela, Alan Williams, Alexander Scheinker

Classifier-pruned Bayesian optimization for particle accelerator tuning: Exploring temporally structured manifold of 6D beam phase space

Read the original on arXiv Machine Learning →

arXiv:2412. 01748v2 Announce Type: replace Abstract: Complex dynamical systems, such as particle accelerators, often require intricate and time-consuming tuning procedures to achieve optimal performance.

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

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Jul 31

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arXiv:2602. 23050v2 Announce Type: replace Abstract: Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data.

By Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt

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arXiv Machine Learning

Jul 3

Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs

arXiv:2603. 12676v3 Announce Type: replace Abstract: Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable.

By Zhangyong Liang, Huanhuan Gao

rag benchmarks

arXiv Machine Learning

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A Multi-stage Constrained Optimization Framework for Data-driven Problems

arXiv:2607. 23480v1 Announce Type: new Abstract: Variational autoencoders (VAEs) transform high-dimensional, often noisy data into a compact latent representation, making downstream optimization more tractable.

By Ye Shi

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arXiv Machine Learning

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SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting

arXiv:2606. 09917v1 Announce Type: new Abstract: Multivariate time series forecasting requires capturing the continuously evolving correlation structure among interacting variables.

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rag benchmarks

arXiv Machine Learning

Jul 31

A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation

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By Liangyu Wu, Qibin Liu, Alexander Yue, Julia Gonski

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arXiv Machine Learning

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mlr3mbo: Bayesian Optimization in R

arXiv:2603. 29730v2 Announce Type: replace-cross Abstract: We present mlr3mbo, a modular toolbox for Bayesian optimization in R.

By Marc Becker, Lennart Schneider, Martin Binder, Lars Kotthoff, Bernd Bischl

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