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Batch process optimization via run-to-run constraints adaptation

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Batch process optimization via run-to-run constraints adaptation

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Predicting in Uncertain Environments: Methods for Robust Machine Learning

Utility/privacy trade-off as regularized optimal transport

Maximum-likelihood estimation of kinetic parameters via the extent-based incremental approach

Analysis of output modifier adaptation for real-time optimization

Very-High-Speed Miniaturized Permanent Magnet Motors: Design and Optimization

Distributed Coupled Learning Over Adaptive Networks

Dynamic Optimization of Constrained Semi-Batch Processes Using Pontryagin’s Minimum Principle – An Effective Quasi-Newton Approach

Exact Convex Modeling of the Optimal Power Flow for the Operation and Planning of Active Distribution Networks with Energy Storage Systems

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Dynamic Optimization of Constrained Semi-Batch Processes Using Pontryagin’s Minimum Principle – An Effective Quasi-Newton Approach

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Maximum-likelihood estimation of kinetic parameters via the extent-based incremental approach

Very-High-Speed Miniaturized Permanent Magnet Motors: Design and Optimization

Utility/privacy trade-off as regularized optimal transport

Analysis of output modifier adaptation for real-time optimization

Distributed Coupled Learning Over Adaptive Networks

Exact Convex Modeling of the Optimal Power Flow for the Operation and Planning of Active Distribution Networks with Energy Storage Systems

Dynamic Optimization of Constrained Semi-Batch Processes Using Pontryagin’s Minimum Principle – An Effective Quasi-Newton Approach

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Dynamic Optimization of Constrained Semi-Batch Processes Using Pontryagin’s Minimum Principle – An Effective Quasi-Newton Approach