Ravutla, S.

Authored

(347b) Sampling-Based Vs. Surrogate-Based Techniques for Data-Driven Optimization: A Comparative Study of Adaptive Sampling and Hybrid-Modeling Approaches
2023 AIChE Annual Meeting (ISBN: 978-0-8169-1120-2)
(63c) A Simultaneous Material Screening and Process Optimization Approach for Carbon Capture Using Machine Learning
2023 Spring Meeting and 19th Global Congress on Process Safety
(104a) Multi-Fidelity Surrogate Models to Generate Low-Fidelity Data for Data-Driven Branch-and-Bound Optimization
2023 Spring Meeting and 19th Global Congress on Process Safety
(234e) Global Convergence Analysis of Data-Driven Spatial Branch-and-Bound Algorithms
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(104e) Efficient Optimization and Accurate Approximation Using Surrogate Models – Tools and Case Studies from RAPID Synopsis Project
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(40f) Pyddsbb: A Python Package for Simulation-Optimization Using Data-Driven Branch-and-Bound Techniques
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)

Related Proceedings

2021 Annual Meeting2022 Annual Meeting2023 AIChE Annual Meeting2023 Spring Meeting and 19th Global Congress on Process Safety