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BibTex format

@inbook{Ceccon:2022:10.1016/B978-0-323-85159-6.50008-7,
author = {Ceccon, F and Jalving, J and Haddad, J and Thebelt, A and Tsay, C and Laird, CD and Misener, R},
booktitle = {Computer Aided Chemical Engineering},
doi = {10.1016/B978-0-323-85159-6.50008-7},
pages = {57--58},
title = {Presentation abstract: Optimization formulations for machine learning surrogates},
url = {http://dx.doi.org/10.1016/B978-0-323-85159-6.50008-7},
year = {2022}
}

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TY  - CHAP
AB  - In many process systems engineering applications, we seek to integrate surrogate models, e.g. already-trained neural network and gradient-boosted tree models, into larger decision-making problems. This presentation explores different ways to automatically take the machine learning surrogate model and produce an optimization formulation. Our goal is to automate the entire workflow of decision-making with surrogate models from input data to optimization formulation. This presentation discusses our progress towards this goal, gives examples of previous successes, and elicits a conversation with colleagues about the path forward.
AU  - Ceccon,F
AU  - Jalving,J
AU  - Haddad,J
AU  - Thebelt,A
AU  - Tsay,C
AU  - Laird,CD
AU  - Misener,R
DO  - 10.1016/B978-0-323-85159-6.50008-7
EP  - 58
PY  - 2022///
SP  - 57
TI  - Presentation abstract: Optimization formulations for machine learning surrogates
T1  - Computer Aided Chemical Engineering
UR  - http://dx.doi.org/10.1016/B978-0-323-85159-6.50008-7
ER  -

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