2 citations · 2 across the 1 of their papers we have counts for
6 papers · 1 filter
WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling
Zhongzheng Li, Qingsong Ran, Shikun Feng +5
Black-box optimization problems remain challenging because of large, weakly structured, and high-dimensional search spaces. Existing methods often suffer from poor sample efficienc…
MCCE: A Framework for Multi-LLM Collaborative Search in Discrete Spaces with Similarity-Filtered Preference Learning
Nian Ran, Zhongzheng Li, Yue Wang +5
Multi-objective discrete optimization problems, such as molecular design, pose significant challenges due to their vast and unstructured combinatorial spaces. Traditional evolution…
Automated Flow Pattern Classification in Multi-phase Systems Using AI and Capacitance Sensing Techniques
Nian Ran, Fayez M. Al-Alweet, Richard Allmendinger +1
In multiphase flow systems, classifying flow patterns is crucial to optimize fluid dynamics and enhance system efficiency. Current industrial methods and scientific laboratories ma…
ExLLM: Experience-Enhanced LLM Optimization for Molecular Design and Beyond
Nian Ran, Yue Wang, Xiaoyuan Zhang +4
Molecular design involves an enormous and irregular search space, where traditional optimizers such as Bayesian optimization, genetic algorithms, and generative models struggle to…
HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting
Nian Ran, Peng Xiao, Yue Wang +4
The application of large deep learning models in weather forecasting has led to significant advancements in the field, including higher-resolution forecasting and extended predicti…
Multi-objective evolutionary GAN for tabular data synthesis
Nian Ran, Bahrul Ilmi Nasution, Claire Little +2
Synthetic data has a key role to play in data sharing by statistical agencies and other generators of statistical data products. Generative Adversarial Networks (GANs), typically a…