mlsanityData to 14 Oct 2026

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

arXiv 2209.10861 ↗First posted 22 Sep 2022cs.LG math.DScited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 19 of its 49 references that are in the map. First posted on arXiv 22 Sep 2022.

Builds on

19 of 49 references.

References · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  5. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  6. 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,262
  7. 2021Characterizing possible failure modes in physics-informed neural networkscited by 643
  8. 2017OptNet: Differentiable Optimization as a Layer in Neural Networkscited by 599
  9. 2021Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networkscited by 527
  10. 2020AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularitycited by 144
  11. 2019Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discoverycited by 93
  12. 2020Fusing Physics-based and Deep Learning Models for Prognosticscited by 40
7 more
  1. 2020Physics guided machine learning using simplified theoriescited by 23
  2. 2022Physics guided neural networks for modelling of non-linear dynamicscited by 20
  3. 2021Deep neural network enabled corrective source term approach to hybrid analysis and modelingcited by 16
  4. 2020Data-Driven Discovery of Coarse-Grained Equationscited by 14
  5. 2020Deep-learning based discovery of partial differential equations in integral form from sparse and noisy datacited by 14
  6. 2022Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approachcited by 6
  7. 2022Sparse deep neural networks for modeling aluminum electrolysis dynamicscited by 3

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.

4 papers.

  1. 2022Artificial intelligence-driven digital twin of a modern house demonstrated in virtual realitycited by 4
  2. 2023Sparse neural networks with skip-connections for identification of aluminum electrolysis cellcited by 1
  3. 2023Deep active learning for nonlinear system identificationcited by 1
  4. 2025Hybrid Modeling, Sim-to-Real Reinforcement Learning, and Large Language Model Driven Control for Digital Twinscited by 1