Data-driven surrogate modelling and benchmarking for process equipment

• URL: http://arxiv.org/abs/2003.07701v2
• Date: Tue, 8 Sep 2020 14:42:14 GMT
• Title: Data-driven surrogate modelling and benchmarking for process equipment
• Authors: Gabriel F. N. Gon\c{c}alves, Assen Batchvarov, Yuyi Liu, Yuxin Liu, Lachlan Mason, Indranil Pan, Omar K. Matar
• Abstract summary: A suite of computational fluid dynamics (CFD) simulations geared toward chemical process equipment modeling has been developed. Various regression-based active learning strategies are explored with these CFD simulators in-the-loop under the constraints of a limited function evaluation budget.
• Score: 1.8395181176356432
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization. A suite of computational fluid dynamics (CFD) simulations geared toward chemical process equipment modeling has been developed and validated with experimental results from the literature. Various regression-based active learning strategies are explored with these CFD simulators in-the-loop under the constraints of a limited function evaluation budget. Specifically, five different sampling strategies and five regression techniques are compared, considering a set of four test cases of industrial significance and varying complexity. Gaussian process regression was observed to have a consistently good performance for these applications. The present quantitative study outlines the pros and cons of the different available techniques and highlights the best practices for their adoption. The test cases and tools are available with an open-source license to ensure reproducibility and engage the wider research community in contributing to both the CFD models and developing and benchmarking new improved algorithms tailored to this field.

Related papers

• SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models [54.78329741186446]
  We propose a novel paradigm that uses a code-based critic model to guide steps including question-code data construction, quality control, and complementary evaluation. Experiments across both in-domain and out-of-domain benchmarks in English and Chinese demonstrate the effectiveness of the proposed paradigm.
  arXiv  Detail & Related papers  (2024-08-28T06:33:03Z)
• Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems [0.8388591755871736]
  Intrusion Detection Systems (IDS) have been continuously improved. Many of them incorporate machine learning (ML) techniques to identify threats. This article aims to present a study that fills this research gap.
  arXiv  Detail & Related papers  (2024-07-15T14:30:25Z)
• Comprehensive Survey of Model Compression and Speed up for Vision Transformers [5.592810604696031]
  Vision Transformers (ViT) have marked a paradigm shift in computer vision, outperforming state-of-the-art models across diverse tasks. However, their practical deployment is hampered by high computational and memory demands. This study addresses the challenge by evaluating four primary model compression techniques.
  arXiv  Detail & Related papers  (2024-04-16T09:19:11Z)
• A Thorough Examination of Decoding Methods in the Era of LLMs [72.65956436513241]
  Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers. This paper provides a comprehensive and multifaceted analysis of various decoding methods within the context of large language models. Our findings reveal that decoding method performance is notably task-dependent and influenced by factors such as alignment, model size, and quantization.
  arXiv  Detail & Related papers  (2024-02-10T11:14:53Z)
• Identifying Simulation Model Through Alternative Techniques for a Medical Device Assembly Process [0.0]
  This scientific paper explores two distinct approaches for identifying and approximating the simulation model. Our goal is to create adaptable models that accurately represent the snap process and can accommodate diverse scenarios.
  arXiv  Detail & Related papers  (2023-09-26T17:40:29Z)
• Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation [1.6685829157403116]
  This paper presents a methodological framework for training, self-optimising, and self-organising surrogate models. We show that surrogate models can be trained on relatively small amounts of data to approximate the underlying simulations accurately.
  arXiv  Detail & Related papers  (2023-09-22T20:52:50Z)
• Synthetic Data-Based Simulators for Recommender Systems: A Survey [55.60116686945561]
  This survey aims at providing a comprehensive overview of the recent trends in the field of modeling and simulation. We start with the motivation behind the development of frameworks implementing the simulations -- simulators. We provide a new consistent classification of existing simulators based on their functionality, approbation, and industrial effectiveness.
  arXiv  Detail & Related papers  (2022-06-22T19:33:21Z)
• Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes [62.997667081978825]
  The paper presents an approach to process mining providing semi-automatic support to model optimization. A model simplification approach is proposed, which essentially abstracts the raw model at the desired granularity. We aim to demonstrate the capabilities of the technological solution using three datasets from different applications in the healthcare domain.
  arXiv  Detail & Related papers  (2022-06-10T16:20:59Z)
• Supply of engineering techniques and software design patterns in psychoanalysis and psychometrics sciences [0.0]
  The purpose of this study is to introduce software technologies and models and artificial intelligence algorithms to improve the weaknesses of CBT (Cognitive Behavior Therapy) method in psychotherapy. The presentation method for this purpose is the implementation of psychometric experiments in which the hidden human variables are inferred from the answers of tests.
  arXiv  Detail & Related papers  (2021-08-16T08:36:37Z)
• A User's Guide to Calibrating Robotics Simulators [54.85241102329546]
  This paper proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world. We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms. Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms.
  arXiv  Detail & Related papers  (2020-11-17T22:24:26Z)
• Predictive modeling approaches in laser-based material processing [59.04160452043105]
  This study aims to automate and forecast the effect of laser processing on material structures. The focus is centred on the performance of representative statistical and machine learning algorithms. Results can set the basis for a systematic methodology towards reducing material design, testing and production cost.
  arXiv  Detail & Related papers  (2020-06-13T17:28:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.