{"total":16,"rows":[{"id":"2202.02414","title":"OMLT: Optimization & Machine Learning Toolkit","created":"2022-02-04","cited_by_count":35,"area":null},{"id":"2211.06149","title":"Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization","created":"2022-11-11","cited_by_count":16,"area":null},{"id":"2202.00060","title":"SnAKe: Bayesian Optimization with Pathwise Exploration","created":"2022-01-31","cited_by_count":14,"area":null},{"id":"2207.00879","title":"Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces","created":"2022-07-02","cited_by_count":14,"area":null},{"id":"2402.08406","title":"Transition Constrained Bayesian Optimization via Markov Decision Processes","created":"2024-02-13","cited_by_count":7,"area":null},{"id":"2601.12554","title":"Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie","created":"2026-01-18","cited_by_count":5,"area":null},{"id":"2406.02352","title":"System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization","created":"2024-06-04","cited_by_count":3,"area":null},{"id":"2502.15642","title":"Training Neural ODEs Using Fully Discretized Simultaneous Optimization","created":"2025-02-21","cited_by_count":3,"area":null},{"id":"2506.18525","title":"Federated Learning from Molecules to Processes: A Perspective","created":"2025-06-23","cited_by_count":3,"area":null},{"id":"2505.23640","title":"Global optimization of graph acquisition functions for neural architecture search","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2302.00990","title":"Physics Informed Piecewise Linear Neural Networks for Process Optimization","created":"2023-02-02","cited_by_count":1,"area":null},{"id":"2507.01048","title":"3W Dataset 2.0.0: a realistic and public dataset with rare undesirable real events in oil wells","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2401.11217","title":"A Hybrid Approach of Transfer Learning and Physics-Informed Modeling: Improving Dissolved Oxygen Concentration Prediction in an Industrial Wastewater Treatment Plant","created":"2024-01-20","cited_by_count":0,"area":null},{"id":"2505.03552","title":"Efficient Training of Physics-enhanced Neural ODEs via Direct Collocation and Nonlinear Programming","created":"2025-05-06","cited_by_count":0,"area":null},{"id":"2605.05382","title":"Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2610.02014","title":"Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}