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Laser Scan Path Design for Controlled Microstructure in Additive Manufacturing with Integrated Reduced-Order Phase-Field Modeling and Deep Reinforcement Learning
Augustine Twumasi, Prokash Chandra Roy, Zixun Li, Soumya Shouvik Bhattacharjee, Zhengtao Gan
https://arxiv.org/abs/2506.21815 …

Laser Scan Path Design for Controlled Microstructure in Additive Manufacturing with Integrated Reduced-Order Phase-Field Modeling and Deep Reinforcement Learning
Laser powder bed fusion (L-PBF) is a widely recognized additive manufacturing technology for producing intricate metal components with exceptional accuracy. A key challenge in L-PBF is the formation of complex microstructures affecting product quality. We propose a physics-guided, machine-learning approach to optimize scan paths for desired microstructure outcomes, such as equiaxed grains. We utilized a phase-field method (PFM) to model crystalline grain structure evolution. To reduce computati…

PUL: Pre-load in Software for Caches Wouldn't Always Play Along
Arthur Bernhardt, Sajjad Tamimi, Florian Stock, Andreas Koch, Ilia Petrov
https://arxiv.org/abs/2506.16976

PUL: Pre-load in Software for Caches Wouldn't Always Play Along
Memory latencies and bandwidth are major factors, limiting system performance and scalability. Modern CPUs aim at hiding latencies by employing large caches, out-of-order execution, or complex hardware prefetchers. However, software-based prefetching exhibits higher efficiency, improving with newer CPU generations. In this paper we investigate software-based, post-Moore systems that offload operations to intelligent memories. We show that software-based prefetching has even higher potential i…

A Bayesian Semiparametric Mixture Model for Clustering Zero-Inflated Microbiome Data
Suppapat Korsurat, Matthew D. Koslovsky
https://arxiv.org/abs/2508.14184 https://

A Bayesian Semiparametric Mixture Model for Clustering Zero-Inflated Microbiome Data
Microbiome research has immense potential for unlocking insights into human health and disease. A common goal in human microbiome research is identifying subgroups of individuals with similar microbial composition that may be linked to specific health states or environmental exposures. However, existing clustering methods are often not equipped to accommodate the complex structure of microbiome data and typically make limiting assumptions regarding the number of clusters in the data which can b…