Max Mowbray

The University of Manchester

United Kingdom

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Articles (10)

A reinforcement learning‐based hybrid modeling framework for bioprocess kinetics identification

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Year:

2023

Distributional reinforcement learning for inventory management in multi-echelon supply chains

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Year:

2023

Industrial data science – a review of machine learning applications for chemical and process industries

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Year:

2022

Integrating Autoencoder and Heteroscedastic Noise Neural Networks for the Batch Process Soft-Sensor Design

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Year:

2022

Probabilistic machine learning based soft-sensors for product quality prediction in batch processes

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Year:

2022

Frontal fibrosing alopecia: survey of severity assessment methods in routine clinical practice and validation of the International Frontal Fibrosing Alopecia Cooperative Group measurement guidance

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Year:

2022

Safe chance constrained reinforcement learning for batch process control

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Year:

2022

Industrial data science – a review of machine learning applications for chemical and process industries

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Year:

2022

A two-step multivariate statistical learning approach for batch process soft sensing

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Year:

2021

Constrained model-free reinforcement learning for process optimization

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Year:

2021

Collaborators (3)

Ehecatl Antonio del Rio Chanona

Lecturer

Imperial College London

UNITED KINGDOM

Philip A. Martin

The University of Manchester

UNITED KINGDOM

Dongda Zhang

University Lecturer

The University of Manchester

UNITED KINGDOM

Max Mowbray

Research Interests

Explore related searches

Contact this professor

Articles (10)

A reinforcement learning‐based hybrid modeling framework for bioprocess kinetics identification

Distributional reinforcement learning for inventory management in multi-echelon supply chains

Industrial data science – a review of machine learning applications for chemical and process industries

Integrating Autoencoder and Heteroscedastic Noise Neural Networks for the Batch Process Soft-Sensor Design

Probabilistic machine learning based soft-sensors for product quality prediction in batch processes

Frontal fibrosing alopecia: survey of severity assessment methods in routine clinical practice and validation of the International Frontal Fibrosing Alopecia Cooperative Group measurement guidance

Safe chance constrained reinforcement learning for batch process control

Industrial data science – a review of machine learning applications for chemical and process industries

A two-step multivariate statistical learning approach for batch process soft sensing

Constrained model-free reinforcement learning for process optimization

Collaborators (3)

Ehecatl Antonio del Rio Chanona

Philip A. Martin

Dongda Zhang

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