Introducing Hybrid Modeling with Time-series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization

• URL: http://arxiv.org/abs/2308.05749v1
• Date: Tue, 25 Jul 2023 15:19:51 GMT
• Title: Introducing Hybrid Modeling with Time-series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization
• Authors: Niranjan Sitapure, and Joseph S Kwon
• Abstract summary: Most existing digital twins rely on data-driven black-box models, predominantly using deep neural recurrent, and convolutional neural networks (DNNs, RNNs, and CNNs) to capture the dynamics of chemical systems. Recently, attention-based time-series transformers (TSTs) that leverage multi-headed attention mechanism and positional encoding have shown high predictive performance. First-of-a-kind, TST-based hybrid framework has been developed for batch crystallization, demonstrating improved accuracy and interpretability compared to traditional black-box models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most existing digital twins rely on data-driven black-box models, predominantly using deep neural recurrent, and convolutional neural networks (DNNs, RNNs, and CNNs) to capture the dynamics of chemical systems. However, these models have not seen the light of day, given the hesitance of directly deploying a black-box tool in practice due to safety and operational issues. To tackle this conundrum, hybrid models combining first-principles physics-based dynamics with machine learning (ML) models have increased in popularity as they are considered a 'best of both worlds' approach. That said, existing simple DNN models are not adept at long-term time-series predictions and utilizing contextual information on the trajectory of the process dynamics. Recently, attention-based time-series transformers (TSTs) that leverage multi-headed attention mechanism and positional encoding to capture long-term and short-term changes in process states have shown high predictive performance. Thus, a first-of-a-kind, TST-based hybrid framework has been developed for batch crystallization, demonstrating improved accuracy and interpretability compared to traditional black-box models. Specifically, two different configurations (i.e., series and parallel) of TST-based hybrid models are constructed and compared, which show a normalized-mean-square-error (NMSE) in the range of $[10, 50]\times10^{-4}$ and an $R^2$ value over 0.99. Given the growing adoption of digital twins, next-generation attention-based hybrid models are expected to play a crucial role in shaping the future of chemical manufacturing.

Related papers

• Automatically Learning Hybrid Digital Twins of Dynamical Systems [56.69628749813084]
  Digital Twins (DTs) simulate the states and temporal dynamics of real-world systems. DTs often struggle to generalize to unseen conditions in data-scarce settings. In this paper, we propose an evolutionary algorithm ($textbfHDTwinGen$) to autonomously propose, evaluate, and optimize HDTwins.
  arXiv  Detail & Related papers  (2024-10-31T07:28:22Z)
• Oscillatory State-Space Models [61.923849241099184]
  We propose Lineary State-Space models (LinOSS) for efficiently learning on long sequences. A stable discretization, integrated over time using fast associative parallel scans, yields the proposed state-space model. We show that LinOSS is universal, i.e., it can approximate any continuous and causal operator mapping between time-varying functions.
  arXiv  Detail & Related papers  (2024-10-04T22:00:13Z)
• Latent Space Energy-based Neural ODEs [73.01344439786524]
  This paper introduces a novel family of deep dynamical models designed to represent continuous-time sequence data. We train the model using maximum likelihood estimation with Markov chain Monte Carlo. Experiments on oscillating systems, videos and real-world state sequences (MuJoCo) illustrate that ODEs with the learnable energy-based prior outperform existing counterparts.
  arXiv  Detail & Related papers  (2024-09-05T18:14:22Z)
• MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking [51.28485682954006]
  We propose a pure Mamba-based framework (MambaVT) to fully exploit intrinsic-temporal contextual modeling for robust visible-thermal tracking. Specifically, we devise the long-range cross-frame integration component to globally adapt to target appearance variations. Experiments show the significant potential of vision Mamba for RGB-T tracking, with MambaVT achieving state-of-the-art performance on four mainstream benchmarks.
  arXiv  Detail & Related papers  (2024-08-15T02:29:00Z)
• Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models [5.37935922811333]
  State Space Models (SSMs) are classical approaches for univariate time series modeling. We present Chimera that uses two input-dependent 2-D SSM heads with different discretization processes to learn long-term progression and seasonal patterns. Our experimental evaluation shows the superior performance of Chimera on extensive and diverse benchmarks.
  arXiv  Detail & Related papers  (2024-06-06T17:58:09Z)
• Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective [63.60312929416228]
  textbftextitAttraos incorporates chaos theory into long-term time series forecasting. We show that Attraos outperforms various LTSF methods on mainstream datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST.
  arXiv  Detail & Related papers  (2024-02-18T05:35:01Z)
• Non-autoregressive Conditional Diffusion Models for Time Series Prediction [3.9722979176564763]
  TimeDiff is a non-autoregressive diffusion model that achieves high-quality time series prediction. We show that TimeDiff consistently outperforms existing time series diffusion models.
  arXiv  Detail & Related papers  (2023-06-08T08:53:59Z)
• Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting [1.1602089225841632]
  The proposed model is composed of two simultaneously trained tracks: the context track and the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchical dilations and equipped with recently proposed attentive recurrent cells. The model produces both point forecasts and predictive intervals.
  arXiv  Detail & Related papers  (2022-12-18T07:42:48Z)
• A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models [2.007262412327553]
  This paper compares two prominent deep learning modeling techniques. The Recurrent Neural Network (RNN)-based Long Short-Term Memory (LSTM) and the convolutional Neural Network (CNN)-based Temporal Convolutional Networks (TCN) are compared.
  arXiv  Detail & Related papers  (2021-12-17T02:46:55Z)
• Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series [43.86737761236125]
  We propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures. A structured inference network is also designed for enabling inductive analysis.
  arXiv  Detail & Related papers  (2021-03-03T04:10:07Z)
• Liquid Time-constant Networks [117.57116214802504]
  We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations.
  arXiv  Detail & Related papers  (2020-06-08T09:53:35Z)

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.