Tootfinder

Hybrid solver methods for ODEs: Machine-Learning combined with standard methods
J\"urgen Geiser
https://arxiv.org/abs/2508.13538 https://arxiv.org/pdf…

Hybrid solver methods for ODEs: Machine-Learning combined with standard methods
In this article, we consider combined standard and machine learning methods to solve ODEs and PDEs. We deal with the minimisation problems for the machine learning algorithms and standard discretization methods, which are related to Runge-Kutta methods and finite difference methods. We show, that we could solve the ODEs with additional ML methods, e.g., feedforward network, such that it will accelerate the solver process.

Relationship Between Leisure Activities, Stress Management Methods, Study Methods, and Methods of Learning New Things Among First-Year Statistics Students
Thiyanga S. Talagala
https://arxiv.org/abs/2508.11726

Relationship Between Leisure Activities, Stress Management Methods, Study Methods, and Methods of Learning New Things Among First-Year Statistics Students
The interplay between leisure activities, stress management methods, studying methods, and methods of learning new things is crucial and affects performance in all aspects of life. On the other hand, data science and statistics are rapidly growing fields with high demands across universities. Thus, this study aimed to identify the similarities and dissimilarities between the four dimensions: leisure activities, stress management methods, studying methods and methods of learning new things. The …

Organic beekeeping can be even more profitable than conventional methods
https://phys.org/news/2025-09-beekeeping-profitable-conventional-methods.html#google_vignette

#hamstercontent im WDR-zeitzeichen: "Laborheld und Kinderliebling: Siegeszug des Goldhamsters" https://www1.wdr.de/mediathek/audio/ze

Fisher-Orthogonal Projection Methods for Natural Gradient Descent with Large Batches
Yishun Lu, Wesley Armour
https://arxiv.org/abs/2508.13898 https://arxi…

Fisher-Orthogonal Projection Methods for Natural Gradient Descent with Large Batches
Modern GPUs are equipped with large amounts of high-bandwidth memory, enabling them to support mini-batch sizes of up to tens of thousands of training samples. However, most existing optimizers struggle to perform effectively at such a large batch size. As batch size increases, gradient noise decreases due to averaging over many samples, limiting the ability of first-order methods to escape sharp or suboptimal minima and reach the global minimum. Meanwhile, second-order methods like the natural…

>> Wald, Gletscher, Seen schwinden<<
Hier ist eine angenehm neutrale Bilderserie über den Klimawandel in Deutschland, die für mich erschreckender wirkt als viele andere Berichte.
https://www.n-tv.de/mediathek/bilderserien

Nonmonotone Trust-Region Methods for Optimization of Set-Valued Mapping of Finite Cardinality
Suprova Ghosh, Debdas Ghosh, Zai-Yun Peng, Xian-Jun Lon
https://arxiv.org/abs/2509.14426

Nonmonotone Trust-Region Methods for Optimization of Set-Valued Mapping of Finite Cardinality
Non-monotone trust-region methods are known to provide additional benefits for scalar and multi-objective optimization, such as enhancing the probability of convergence and improving the speed of convergence. For optimization of set-valued maps, non-monotone trust-region methods have not yet been explored and investigated to see if they show similar benefits. Thus, in this article, we propose two non-monotone trust-region schemes--max-type and average-type for set-valued optimization. Using the…

SCRNet: Spatial-Channel Regulation Network for Medical Ultrasound Image Segmentation
Weixin Xu, Ziliang Wang
https://arxiv.org/abs/2508.13899 https://arxiv…

SCRNet: Spatial-Channel Regulation Network for Medical Ultrasound Image Segmentation
Medical ultrasound image segmentation presents a formidable challenge in the realm of computer vision. Traditional approaches rely on Convolutional Neural Networks (CNNs) and Transformer-based methods to address the intricacies of medical image segmentation. Nevertheless, inherent limitations persist, as CNN-based methods tend to disregard long-range dependencies, while Transformer-based methods may overlook local contextual information. To address these deficiencies, we propose a novel Feature…

Surrogate-based Bayesian calibration methods for climate models: a comparison of traditional and non-traditional approaches
Maike F. Holthuijzen, Atlanta Chakraborty, Elizabeth Krath, Tommie Catanach
https://arxiv.org/abs/2508.13071

Surrogate-based Bayesian calibration methods for climate models: a comparison of traditional and non-traditional approaches
Parameter calibration is crucial for reducing uncertainty and improving simulation accuracy in physics-based models, yet computational constraints pose significant challenges. Bayesian calibration methods offer a principled framework for combining prior knowledge with data while rigorously quantifying uncertainty. In this work, we compare four emulator-based Bayesian calibration methods: Calibrate-Emulate-Sample (CES), History Matching (HM), Bayesian Optimal Experimental Design (BOED), and a no…

Okay, I think I'm done with the `dice-roller` code for the time being. It now supports two different "methods" for weighting dice, and I even documented all of the methods. Lot of fun to write, and it should make it into the `pathfinder2e-tui` program over the next week.
Also, very certain this is technically a dice rolling language at this point.