A novel corrective-source term approach to modeling unknown physics in aluminum extraction process

• URL: http://arxiv.org/abs/2209.10861v1
• Date: Thu, 22 Sep 2022 08:45:50 GMT
• Title: A novel corrective-source term approach to modeling unknown physics in aluminum extraction process
• Authors: Haakon Robinson, Erlend Lundby, Adil Rasheed, Jan Tommy Gravdahl
• Abstract summary: We investigate the Corrective Source Term Approach (CoSTA), which uses a data-driven model to correct a misspecified physics-based model. This enables us to develop models that make accurate predictions even when the underlying physics of the problem is not well understood. We demonstrate that the method improves both accuracy and predictive stability, yielding an overall more trustworthy model.
• Score: 0.5257115841810257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods to fields such as modeling and control. However, despite the flexibility and surprising accuracy of such black-box models, it remains difficult to trust them. Recent efforts to combine the two approaches aim to develop flexible models that nonetheless generalize well; a paradigm we call Hybrid Analysis and modeling (HAM). In this work we investigate the Corrective Source Term Approach (CoSTA), which uses a data-driven model to correct a misspecified physics-based model. This enables us to develop models that make accurate predictions even when the underlying physics of the problem is not well understood. We apply CoSTA to model the Hall-H\'eroult process in an aluminum electrolysis cell. We demonstrate that the method improves both accuracy and predictive stability, yielding an overall more trustworthy model.

Related papers

• Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling [5.437298646956505]
  Building energy modeling is a key tool for optimizing the performance of building energy systems.<n>Recently, hybrid approaches that combine the strengths of both paradigms have gained attention.
  arXiv  Detail & Related papers  (2025-07-23T14:07:33Z)
• Hybrid Adaptive Modeling using Neural Networks Trained with Nonlinear Dynamics Based Features [5.652228574188242]
  This paper introduces a novel approach that departs from standard techniques by uncovering information from nonlinear dynamical modeling and embedding it in data-based models. By explicitly incorporating nonlinear dynamic phenomena through perturbation methods, the predictive capabilities are more realistic and insightful compared to knowledge obtained from brute-force numerical simulations.
  arXiv  Detail & Related papers  (2025-01-21T02:38:28Z)
• Deep learning for model correction of dynamical systems with data scarcity [0.0]
  We present a deep learning framework for correcting existing dynamical system models utilizing only a scarce high-fidelity data set. We focus on the case when the amount of high-fidelity data is so small that most of the existing data driven modeling methods cannot be applied.
  arXiv  Detail & Related papers  (2024-10-23T14:33:11Z)
• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• Towards Learning Stochastic Population Models by Gradient Descent [0.0]
  We show that simultaneous estimation of parameters and structure poses major challenges for optimization procedures. We demonstrate accurate estimation of models but find that enforcing the inference of parsimonious, interpretable models drastically increases the difficulty.
  arXiv  Detail & Related papers  (2024-04-10T14:38:58Z)
• Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences [6.067007470552307]
  We propose a methodology for finding sequences of machine learning models that are stable across retraining iterations. We develop a mixed-integer optimization formulation that is guaranteed to recover optimal models. Our method shows stronger stability than greedily trained models with a small, controllable sacrifice in predictive power.
  arXiv  Detail & Related papers  (2024-03-28T22:45:38Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [51.8162883997512]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. This creates a barrier to fusing knowledge across individual models to yield a better single model. We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• Learning continuous models for continuous physics [94.42705784823997]
  We develop a test based on numerical analysis theory to validate machine learning models for science and engineering applications. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.
  arXiv  Detail & Related papers  (2022-02-17T07:56:46Z)
• A data-driven peridynamic continuum model for upscaling molecular dynamics [3.1196544696082613]
  We propose a learning framework to extract, from molecular dynamics data, an optimal Linear Peridynamic Solid model. We provide sufficient well-posedness conditions for discretized LPS models with sign-changing influence functions. This framework guarantees that the resulting model is mathematically well-posed, physically consistent, and that it generalizes well to settings that are different from the ones used during training.
  arXiv  Detail & Related papers  (2021-08-04T07:07:47Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Learning from others' mistakes: Avoiding dataset biases without modeling them [111.17078939377313]
  State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended task. Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available. We show a method for training models that learn to ignore these problematic correlations.
  arXiv  Detail & Related papers  (2020-12-02T16:10:54Z)
• VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry [70.10343492784465]
  It is necessary to expose to the process engineer, not solely the model predictions, but also their interpretability. Model-agnostic local interpretability solutions based on LIME have recently emerged to improve the original method. We present in this paper a novel approach, VAE-LIME, for local interpretability of data-driven models forecasting the temperature of the hot metal produced by a blast furnace.
  arXiv  Detail & Related papers  (2020-07-15T07:07:07Z)
• Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: Generalized Formulations [5.827236278192557]
  This study presents a new modeling framework, named physics regularized Gaussian process (PRGP) This novel approach can encode physics models, i.e., classical traffic flow models, into the Gaussian process architecture and so as to regularize the Machine Learning training process. To prove the effectiveness of the proposed model, this paper conducts empirical studies on a real-world dataset that is collected from a stretch of I-15 freeway, Utah.
  arXiv  Detail & Related papers  (2020-07-14T17:27:23Z)
• Hybrid modeling: Applications in real-time diagnosis [64.5040763067757]
  We outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models. We are using such models for real-time diagnosis applications.
  arXiv  Detail & Related papers  (2020-03-04T00:44:57Z)

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.