data-Driven optimization

Multi-fidelity data-driven design and analysis of reactor and tube simulations
Optimizing complex reactor geometries is vital to promote enhanced efficiency. We present a framework to solve this nonlinear, …
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Data-driven coordination of subproblems in enterprise-wide optimization under organizational considerations
While decomposition techniques in mathematical programming are usually designed for numerical efficiency, coordination problems within …
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Constrained model-free reinforcement learning for process optimization
Reinforcement learning (RL) is a control approach that can handle nonlinear stochastic optimal control problems. However, despite the …
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