A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty

• URL: http://arxiv.org/abs/2403.08901v3
• Date: Tue, 14 May 2024 02:17:13 GMT
• Title: A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty
• Authors: Pratyush Kumar Singh, Kathryn A. Farrell-Maupin, Danial Faghihi,
• Abstract summary: This study presents the Occam Plausibility Algorithm for surrogate models (OPAL-surrogate) OPAL-surrogate provides a systematic framework to uncover predictive neural network-based surrogate models. It balances the trade-off between model complexity, accuracy, and prediction uncertainty.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The widespread integration of deep neural networks in developing data-driven surrogate models for high-fidelity simulations of complex physical systems highlights the critical necessity for robust uncertainty quantification techniques and credibility assessment methodologies, ensuring the reliable deployment of surrogate models in consequential decision-making. This study presents the Occam Plausibility Algorithm for surrogate models (OPAL-surrogate), providing a systematic framework to uncover predictive neural network-based surrogate models within the large space of potential models, including various neural network classes and choices of architecture and hyperparameters. The framework is grounded in hierarchical Bayesian inferences and employs model validation tests to evaluate the credibility and prediction reliability of the surrogate models under uncertainty. Leveraging these principles, OPAL-surrogate introduces a systematic and efficient strategy for balancing the trade-off between model complexity, accuracy, and prediction uncertainty. The effectiveness of OPAL-surrogate is demonstrated through two modeling problems, including the deformation of porous materials for building insulation and turbulent combustion flow for the ablation of solid fuels within hybrid rocket motors.

Related papers

• The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness [50.52507648690234]
  Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model. We introduce three robustness indicators and conduct experiments across diverse robust datasets. Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
  arXiv  Detail & Related papers  (2024-01-25T09:18:51Z)
• DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling [4.76185521514135]
  We introduce a novel method, DiffHybrid-UQ, for effective and efficient uncertainty propagation and estimation in hybrid neural differentiable models. Specifically, our approach effectively discerns and quantifies both aleatoric uncertainties, arising from data noise, and epistemic uncertainties, resulting from model-form discrepancies and data sparsity.
  arXiv  Detail & Related papers  (2023-12-30T07:40:47Z)
• General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation [1.708673732699217]
  Estimating the probability of failure for complex real-world systems is often prohibitively expensive. This paper presents a robust multi-fidelity surrogate modeling strategy. It is shown to be highly accurate while drastically reducing the number of high-fidelity model calls.
  arXiv  Detail & Related papers  (2022-12-07T00:03:21Z)
• Physics-Infused Fuzzy Generative Adversarial Network for Robust Failure Prognosis [0.0]
  FuzzyGAN based method embeds a physics-based model in the aggregation of the fuzzy implications. Results on a bearing problem showcases the efficacy of adding a physics-based aggregation in a fuzzy logic model to improve GAN's ability to model health.
  arXiv  Detail & Related papers  (2022-06-15T18:50:16Z)
• Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems [0.0]
  We propose a physics-guided hybrid approach for modeling non-autonomous systems under control. This is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy. Experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model.
  arXiv  Detail & Related papers  (2022-04-27T14:33:02Z)
• A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training [64.71254710803368]
  Adversarial Training (AT) is an effective approach to enhance the robustness of deep neural networks. We demystify this phenomenon by developing a unified probabilistic framework, called Contrastive Energy-based Models (CEM) We propose a principled method to develop adversarial learning and sampling methods.
  arXiv  Detail & Related papers  (2022-03-25T05:33:34Z)
• Surrogate Modeling for Physical Systems with Preserved Properties and Adjustable Tradeoffs [0.0]
  We present a model-based and a data-driven strategy to generate surrogate models. The latter generates interpretable surrogate models by fitting artificial relations to a presupposed topological structure. Our framework is compatible with various spatial discretization schemes for distributed parameter models.
  arXiv  Detail & Related papers  (2022-02-02T17:07:02Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [55.28436972267793]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• On the model-based stochastic value gradient for continuous reinforcement learning [50.085645237597056]
  We show that simple model-based agents can outperform state-of-the-art model-free agents in terms of both sample-efficiency and final reward. Our findings suggest that model-based policy evaluation deserves closer attention.
  arXiv  Detail & Related papers  (2020-08-28T17:58:29Z)
• Providing reliability in Recommender Systems through Bernoulli Matrix Factorization [63.732639864601914]
  This paper proposes Bernoulli Matrix Factorization (BeMF) to provide both prediction values and reliability values. BeMF acts on model-based collaborative filtering rather than on memory-based filtering. The more reliable a prediction is, the less liable it is to be wrong.
  arXiv  Detail & Related papers  (2020-06-05T14:24:27Z)

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.