Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression

• URL: http://arxiv.org/abs/2211.10360v1
• Date: Wed, 16 Nov 2022 02:31:57 GMT
• Title: Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression
• Authors: Harsh Vardhan, Umesh Timalsina, Peter Volgyesi, Janos Sztipanovits
• Abstract summary: We propose a simple and scalable approach for active learning that works in a student-teacher manner to train a surrogate model. By using this proposed approach, we are able to achieve the same level of surrogate accuracy as the other baselines like DBAL and Monte Carlo sampling.
• Score: 0.6021787236982659
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a computer-aided engineering design optimization problem that involves notoriously complex and time-consuming simulator, the prevalent approach is to replace these simulations with a data-driven surrogate that approximates the simulator's behavior at a much cheaper cost. The main challenge in creating an inexpensive data-driven surrogate is the generation of a sheer number of data using these computationally expensive numerical simulations. In such cases, Active Learning (AL) methods have been used that attempt to learn an input--output behavior while labeling the fewest samples possible. The current trend in AL for a regression problem is dominated by the Bayesian framework that needs training an ensemble of learning models that makes surrogate training computationally tedious if the underlying learning model is Deep Neural Networks (DNNs). However, DNNs have an excellent capability to learn highly nonlinear and complex relationships even for a very high dimensional problem. To leverage the excellent learning capability of deep networks along with avoiding the computational complexity of the Bayesian paradigm, in this work we propose a simple and scalable approach for active learning that works in a student-teacher manner to train a surrogate model. By using this proposed approach, we are able to achieve the same level of surrogate accuracy as the other baselines like DBAL and Monte Carlo sampling with up to 40 % fewer samples. We empirically evaluated this method on multiple use cases including three different engineering design domains:finite element analysis, computational fluid dynamics, and propeller design.

Related papers

• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling [0.0]
  Convolutional neural networks (CNNs) have gained popularity as the basis for such surrogate models. We propose training a CNN surrogate model on a mixture of numerical solutions to both the $d$-dimensional problem and its ($d-1$)-dimensional approximation. We demonstrate our approach on a multiphase flow test problem, using transfer learning to train a dense fully-convolutional encoder-decoder CNN on the two classes of data.
  arXiv  Detail & Related papers  (2024-10-16T05:07:48Z)
• Machine learning surrogates for efficient hydrologic modeling: Insights from stochastic simulations of managed aquifer recharge [0.0]
  We propose a hybrid modeling workflow for process-based hydrologic models and machine learning surrogate models. As a case study, we apply this workflow to simulations of variably saturated groundwater flow at a prospective managed aquifer recharge site. Our results demonstrate that ML surrogate models can achieve under 10% mean absolute percentage error and yield order-of-magnitude runtime savings.
  arXiv  Detail & Related papers  (2024-07-30T15:24:27Z)
• Towards Robust Dataset Learning [90.2590325441068]
  We propose a principled, tri-level optimization to formulate the robust dataset learning problem. Under an abstraction model that characterizes robust vs. non-robust features, the proposed method provably learns a robust dataset.
  arXiv  Detail & Related papers  (2022-11-19T17:06:10Z)
• Simulation-Based Parallel Training [55.41644538483948]
  We present our ongoing work to design a training framework that alleviates those bottlenecks. It generates data in parallel with the training process. We present a strategy to mitigate this bias with a memory buffer.
  arXiv  Detail & Related papers  (2022-11-08T09:31:25Z)
• DeepAL for Regression Using $\epsilon$-weighted Hybrid Query Strategy [0.799536002595393]
  We propose a novel sampling technique by combining the active learning (AL) method with Deep Learning (DL) We call this method $epsilon$-weighted hybrid query strategy ($epsilon$-HQS). During the empirical evaluation, better accuracy of the surrogate was observed in comparison to other methods of sample selection.
  arXiv  Detail & Related papers  (2022-06-24T14:38:05Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Deep Learning-based FEA surrogate for sub-sea pressure vessel [0.799536002595393]
  A pressure vessel contains electronics, power sources, and other sensors that can not be flooded. A traditional design approach for a pressure vessel design involves running multiple Finite Element Analysis (FEA) based simulations. Running these FEAs are computationally very costly for any optimization process. A better approach is the surrogate design with the goal of replacing FEA-based prediction with some learning-based regressor.
  arXiv  Detail & Related papers  (2022-06-06T00:47:10Z)
• Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models [0.0]
  To construct data-driven surrogate models, several thousand high-fidelity simulation runs may be required to provide training samples. We present a framework where most of the training simulations are performed on coarsened geomodels. The network provides results that are significantly more accurate than the low-fidelity simulations used for most of the training.
  arXiv  Detail & Related papers  (2022-04-23T20:09:49Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)

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.