Tootfinder

PGR-DRC: Pre-Global Routing DRC Violation Prediction Using Unsupervised Learning
Riadul Islam, Dhandeep Challagundla
https://arxiv.org/abs/2507.13355 https…

PGR-DRC: Pre-Global Routing DRC Violation Prediction Using Unsupervised Learning
Leveraging artificial intelligence (AI)-driven electronic design and automation (EDA) tools, high-performance computing, and parallelized algorithms are essential for next-generation microprocessor innovation, ensuring continued progress in computing, AI, and semiconductor technology. Machine learning-based design rule checking (DRC) and lithography hotspot detection can improve first-pass silicon success. However, conventional ML and neural network (NN)-based models use supervised learning and…

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks
Guobin Zhao, Pengyu Zhao, Yongchul G. Chung
https://arxiv.org/abs/2506.14845

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks
The computational discovery and design of new crystalline materials, particularly metal-organic frameworks (MOFs), heavily relies on high-quality, computation-ready structural data. However, recent studies have revealed significant error rates within existing MOF databases, posing a critical data problem that hinders efficient high-throughput computational screening. While rule-based algorithms like MOSAEC, MOFChecker, and the Chen and Manz method (Chen-Manz) have been developed to address this…

Improving Q-Learning for Real-World Control: A Case Study in Series Hybrid Agricultural Tractors
Hend Abououf, Sidra Ghayour Bhatti, Qadeer Ahmed
https://arxiv.org/abs/2508.03647

Improving Q-Learning for Real-World Control: A Case Study in Series Hybrid Agricultural Tractors
The variable and unpredictable load demands in hybrid agricultural tractors make it difficult to design optimal rule-based energy management strategies, motivating the use of adaptive, learning-based control. However, existing approaches often rely on basic fuel-based rewards and do not leverage expert demonstrations to accelerate training. In this paper, first, the performance of Q-value-based reinforcement learning algorithms is evaluated for powertrain control in a hybrid agricultural tracto…

Replaced article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- Rule-Based Graph Programs Matching the Time Complexity of Imperative Algorithms
Ziad Ismaili Alaoui, Detlef Plump

Replaced article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- Rule-Based Graph Programs Matching the Time Complexity of Imperative Algorithms
Ziad Ismaili Alaoui, Detlef Plump