A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Nirupaplava Metta, Rohit Ramachandran, Marianthi Ierapetritou

Research output: Contribution to journalArticlepeer-review

Abstract

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.

Original languageEnglish (US)
Article numbere17095
JournalAIChE Journal
Volume67
Issue number2
DOIs
StatePublished - Feb 2021

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Environmental Engineering
  • Chemical Engineering(all)

Keywords

  • adaptive sampling
  • black-box modeling
  • feasibility analysis
  • neural networks
  • surrogate model

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