Generative ai and process systems engineering: The next frontier
This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …
such as large language models (LLMs), can enhance solution methodologies within process …
From distribution learning in training to gradient search in testing for combinatorial optimization
Extensive experiments have gradually revealed the potential performance bottleneck of
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …
Reinforcement Learning within Tree Search for Fast Macro Placement
Macro placement is a crucial step in modern chip design, and reinforcement learning (RL)
has recently emerged as a promising technique for improving the placement quality …
has recently emerged as a promising technique for improving the placement quality …
Mixsatgen: Learning graph mixing for sat instance generation
The Boolean satisfiability problem (SAT) stands as a canonical NP-complete task. In
particular, the scarcity of real-world SAT instances and their usefulness for tuning SAT …
particular, the scarcity of real-world SAT instances and their usefulness for tuning SAT …
Machine learning insides optverse ai solver: Design principles and applications
In an era of digital ubiquity, efficient resource management and decision-making are
paramount across numerous industries. To this end, we present a comprehensive study on …
paramount across numerous industries. To this end, we present a comprehensive study on …
Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling
Learning neural operators for solving partial differential equations (PDEs) has attracted
great attention due to its high inference efficiency. However, training such operators requires …
great attention due to its high inference efficiency. However, training such operators requires …
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
Logic Synthesis (LS) plays a vital role in chip design--a cornerstone of the semiconductor
industry. A key task in LS is to transform circuits--modeled by directed acyclic graphs (DAGs) …
industry. A key task in LS is to transform circuits--modeled by directed acyclic graphs (DAGs) …
TransEdge: Task Offloading with GNN and DRL in Edge Computing-Enabled Transportation Systems
A Xu, Z Hu, X Li, R Tian, X Zhang… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
In recent years, since edge computing has improved the performance of transportation
systems, research on edge computing-enabled transportation systems has received …
systems, research on edge computing-enabled transportation systems has received …
Learning to Generate Scalable MILP Instances
Large-scale Mixed-Integer Linear Programming (MILP) problems have been efficiently
addressed using Machine Learning (ML)-based frameworks, especially ML-based …
addressed using Machine Learning (ML)-based frameworks, especially ML-based …
ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation
Data plays a pivotal role in the development of both classic and learning-based methods for
Mixed-Integer Linear Programming (MILP). However, the scarcity of data in real-world …
Mixed-Integer Linear Programming (MILP). However, the scarcity of data in real-world …