Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis

• URL: http://arxiv.org/abs/2405.18444v1
• Date: Fri, 24 May 2024 14:10:22 GMT
• Title: Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis
• Authors: Geremy Loachamín Suntaxi, Paris Papavasileiou, Eleni D. Koronaki, Dimitrios G. Giovanis, Georgios Gakis, Ioannis G. Aviziotis, Martin Kathrein, Gabriele Pozzetti, Christoph Czettl, Stéphane P. A. Bordas, Andreas G. Boudouvis,
• Abstract summary: This work introduces a comprehensive approach to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors. Our methodology relies on process outcomes, derived by a detailed CFD model, to identify clusters of "outcomes" corresponding to distinct process regimes. This phenomenon is experimentally validated through Arrhenius plot analysis, affirming the efficacy of our approach.
• Score: 0.1558630944877332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work introduces a comprehensive approach utilizing data-driven methods to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors and the interplay of physical mechanism that dominate in each one of them. Through this work, we address three key objectives. Firstly, our methodology relies on process outcomes, derived by a detailed CFD model, to identify clusters of "outcomes" corresponding to distinct process regimes, wherein the relative influence of input variables undergoes notable shifts. This phenomenon is experimentally validated through Arrhenius plot analysis, affirming the efficacy of our approach. Secondly, we demonstrate the development of an efficient surrogate model, based on Polynomial Chaos Expansion (PCE), that maintains accuracy, facilitating streamlined computational analyses. Finally, as a result of PCE, sensitivity analysis is made possible by means of Sobol' indices, that quantify the impact of process inputs across identified regimes. The insights gained from our analysis contribute to the formulation of hypotheses regarding phenomena occurring beyond the transition regime. Notably, the significance of temperature even in the diffusion-limited regime, as evidenced by the Arrhenius plot, suggests activation of gas phase reactions at elevated temperatures. Importantly, our proposed methods yield insights that align with experimental observations and theoretical principles, aiding decision-making in process design and optimization. By circumventing the need for costly and time-consuming experiments, our approach offers a pragmatic pathway towards enhanced process efficiency. Moreover, this study underscores the potential of data-driven computational methods for innovating reactor design paradigms.

Related papers

• On the Design of Ethereum Data Availability Sampling: A Comprehensive Simulation Study [0.0]
  This paper presents an in-depth exploration of Data Availability Sampling (DAS) and sharding mechanisms within decentralized systems through simulation-based analysis. DAS, a pivotal concept in blockchain technology and decentralized networks, is thoroughly examined to unravel its intricacies and assess its impact on system performance. A series of experiments are conducted within the simulated environment to validate theoretical formulations and dissect the interplay of DAS parameters.
  arXiv  Detail & Related papers  (2024-07-25T14:47:41Z)
• See Further for Parameter Efficient Fine-tuning by Standing on the Shoulders of Decomposition [56.87609859444084]
  parameter-efficient fine-tuning (PEFT) focuses on optimizing a select subset of parameters while keeping the rest fixed, significantly lowering computational and storage overheads. We take the first step to unify all approaches by dissecting them from a decomposition perspective. We introduce two novel PEFT methods alongside a simple yet effective framework designed to enhance the performance of PEFT techniques across various applications.
  arXiv  Detail & Related papers  (2024-07-07T15:44:42Z)
• Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Network [2.6130735302655554]
  We introduce an adjoint sensitivity approach to expedite the learning of mechanistic model parameters. In this paper, we consider enzymatic analysis (SA) representing a multi-scale bioprocess mechanistic model.
  arXiv  Detail & Related papers  (2024-05-07T05:06:45Z)
• PCA for Point Processes [3.4248731707266264]
  We introduce a novel statistical framework for the analysis of replicated point processes. By treating point process realizations as random measures, we adopt a functional analysis perspective. Key theoretical contributions include establishing a Karhunen-Loeve expansion for the random measures.
  arXiv  Detail & Related papers  (2024-04-30T15:57:18Z)
• ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization [52.5587113539404]
  We introduce a causality-aware entropy term that effectively identifies and prioritizes actions with high potential impacts for efficient exploration. Our proposed algorithm, ACE: Off-policy Actor-critic with Causality-aware Entropy regularization, demonstrates a substantial performance advantage across 29 diverse continuous control tasks.
  arXiv  Detail & Related papers  (2024-02-22T13:22:06Z)
• Optimizing $CO_{2}$ Capture in Pressure Swing Adsorption Units: A Deep Neural Network Approach with Optimality Evaluation and Operating Maps for Decision-Making [0.0]
  This study focuses on enhancing Pressure Swing Adsorption units for carbon dioxide capture. We developed and implemented a multiple-input, single-output (MISO) framework comprising two deep neural network (DNN) models. This approach delineated feasible operational regions (FORs) and highlighted the spectrum of optimal decision-making scenarios.
  arXiv  Detail & Related papers  (2023-12-06T19:43:37Z)
• Deep Learning-based Analysis of Basins of Attraction [49.812879456944984]
  This research addresses the challenge of characterizing the complexity and unpredictability of basins within various dynamical systems. The main focus is on demonstrating the efficiency of convolutional neural networks (CNNs) in this field.
  arXiv  Detail & Related papers  (2023-09-27T15:41:12Z)
• Entropic Matching for Expectation Propagation of Markov Jump Processes [38.60042579423602]
  We propose a new tractable inference scheme based on an entropic matching framework. We demonstrate the effectiveness of our method by providing closed-form results for a simple family of approximate distributions. We derive expressions for point estimation of the underlying parameters using an approximate expectation procedure.
  arXiv  Detail & Related papers  (2023-09-27T12:07:21Z)
• Latent Variable Representation for Reinforcement Learning [131.03944557979725]
  It remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of model-based reinforcement learning. We provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models.
  arXiv  Detail & Related papers  (2022-12-17T00:26:31Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• Energy-based View of Retrosynthesis [70.66156081030766]
  We propose a framework that unifies sequence- and graph-based methods as energy-based models. We present a novel dual variant within the framework that performs consistent training over Bayesian forward- and backward-prediction. This model improves state-of-the-art performance by 9.6% for template-free approaches where the reaction type is unknown.
  arXiv  Detail & Related papers  (2020-07-14T18:51:06Z)

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.