6 papers
Large Language Model Based Agent for Automated Discovery in Computational Physics
Hang Lin, Chongwen Liu, Gang Yan
Scientific discovery in computational physics can often be framed as the optimization of quantitatively evaluable objectives subject to physical constraints. While researchers exce…
Quantum Non-Linear Bandit Optimization
Zakaria Shams Siam, Chaowen Guan, Chong Liu
We study non-linear bandit optimization where the learner maximizes a black-box function with zeroth order function oracle, which has been successfully applied in many critical app…
Accelerating PDE Surrogates via RL-Guided Mesh Optimization
Yang Meng, Ruoxi Jiang, Zhuokai Zhao +3
Deep surrogate models for parametric partial differential equations (PDEs) can deliver high-fidelity approximations but remain prohibitively data-hungry: training often requires th…
None To Optima in Few Shots: Bayesian Optimization with MDP Priors
Diantong Li, Kyunghyun Cho, Chong Liu
Bayesian Optimization (BO) is an efficient tool for optimizing black-box functions, but its theoretical guarantees typically hold in the asymptotic regime. In many critical real-wo…
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?
Hwanwoo Kim, Chong Liu, Yuxin Chen
Bayesian optimization (BO) is a widely used iterative algorithm for optimizing black-box functions. Each iteration requires maximizing an acquisition function, such as the upper co…
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
Diantong Li, Fengxue Zhang, Chong Liu +1
Multi-objective Bayesian optimization has been widely adopted in scientific experiment design, including drug discovery and hyperparameter optimization. In practice, regulatory or…