3 papers
cs.LG2026
Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery
Runzhe Liu, Zihao Wang, Wenbo Yang +1
Extracting interpretable governing equations from sparse, noisy chemical time-series data remains difficult because discrete reaction topology and continuous kinetic parameters are…
cs.LG2026
Input Convex Lipschitz Recurrent Neural Networks for Robust and Efficient Process Modeling and Optimization
Zihao Wang, Yuhan Li, Yao Shi +1
Computational efficiency and robustness are essential in process modeling, optimization, and control for real-world engineering applications. While neural network-based approaches…
cs.CE2025
Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation
Zihao Wang, Zhe Wu
Developing accurate models for chemical reactors is often challenging due to the complexity of reaction kinetics and process dynamics. Traditional approaches require retraining mod…