ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering

• URL: http://arxiv.org/abs/2310.02913v1
• Date: Wed, 4 Oct 2023 15:50:05 GMT
• Title: ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering
• Authors: Cristiano Fanelli, James Giroux
• Abstract summary: We introduce a physics-informed Bayesian Neural Network (BNN) with flow approximated posteriors for detailed uncertainty quantification (UQ) at the physics event-level. Applying to Deep Inelastic Scattering (DIS) events, our model effectively extracts the kinematic variables $x$, $Q2$, and $y$. This detailed description of the underlying uncertainty proves invaluable for decision-making, especially in tasks like event filtering.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a physics-informed Bayesian Neural Network (BNN) with flow approximated posteriors using multiplicative normalizing flows (MNF) for detailed uncertainty quantification (UQ) at the physics event-level. Our method is capable of identifying both heteroskedastic aleatoric and epistemic uncertainties, providing granular physical insights. Applied to Deep Inelastic Scattering (DIS) events, our model effectively extracts the kinematic variables $x$, $Q^2$, and $y$, matching the performance of recent deep learning regression techniques but with the critical enhancement of event-level UQ. This detailed description of the underlying uncertainty proves invaluable for decision-making, especially in tasks like event filtering. It also allows for the reduction of true inaccuracies without directly accessing the ground truth. A thorough DIS simulation using the H1 detector at HERA indicates possible applications for the future EIC. Additionally, this paves the way for related tasks such as data quality monitoring and anomaly detection. Remarkably, our approach effectively processes large samples at high rates.

Related papers

• Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling [10.055838489452817]
  Deep ensembles (DEs) are efficient and scalable methods for uncertainty quantification (UQ) in Deep Neural Networks (DNNs) We propose a novel method that combines Bayesian optimization (BO) with DE, referred to as BODE, to enhance both predictive accuracy and UQ. We apply BODE to a case study involving a Densely connected Convolutional Neural Network (DCNN) trained on computational fluid dynamics (CFD) data to predict eddy viscosity in sodium fast reactor thermal stratification modeling.
  arXiv  Detail & Related papers  (2024-12-11T21:06:50Z)
• Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning [1.8175282137722093]
  Uncertainty (UQ) in scientific machine learning (SciML) combines the powerful predictive power of SciML with methods for quantifying the reliability of the learned models. We provide a new interpretation for UQ problems by establishing a new theoretical connection between some Bayesian inference problems arising in SciML and viscous Hamilton-Jacobi partial differential equations (HJ PDEs) We develop a new Riccati-based methodology that provides computational advantages when continuously updating the model predictions.
  arXiv  Detail & Related papers  (2024-04-12T20:54:01Z)
• PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction [5.06136344261226]
  CNN-based methods for data fidelity enhancement rely on low-fidelity data patterns and distributions during the training phase. Our proposed model - Physics-informed Residual Diffusion - demonstrates the capability to elevate the quality of data from both standard low-fidelity inputs. Experimental results have shown that our approach can effectively reconstruct high-quality outcomes for two-dimensional turbulent flows without requiring retraining.
  arXiv  Detail & Related papers  (2024-04-12T11:45:51Z)
• IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning [11.5992081385106]
  We propose a novel framework for uncertainty quantification via information bottleneck (IB-UQ) for scientific machine learning tasks. We incorporate the bottleneck by a confidence-aware encoder, which encodes inputs into latent representations according to the confidence of the input data. We also propose a data augmentation based information bottleneck objective which can enhance the quality of the extrapolation uncertainty.
  arXiv  Detail & Related papers  (2023-02-07T05:56:42Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction [45.6432265855424]
  We introduce Neural Uncertainty Quantifier (NUQ) - a principled quantification of the model's predictive uncertainty. Our proposed framework trains more effectively compared to the state-of-theart models.
  arXiv  Detail & Related papers  (2021-10-06T00:25:22Z)
• High Dimensional Level Set Estimation with Bayesian Neural Network [58.684954492439424]
  This paper proposes novel methods to solve the high dimensional Level Set Estimation problems using Bayesian Neural Networks. For each problem, we derive the corresponding theoretic information based acquisition function to sample the data points. Numerical experiments on both synthetic and real-world datasets show that our proposed method can achieve better results compared to existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-12-17T23:21:53Z)
• Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network [0.0]
  We propose a methodology for efficient uncertainty quantification for dynamic subsurface flow with a surrogate constructed by the Theory-guided Neural Network (TgNN) parameters, time and location comprise the input of the neural network, while the quantity of interest is the output. The trained neural network can predict solutions of subsurface flow problems with new parameters.
  arXiv  Detail & Related papers  (2020-04-25T12:41:57Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

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.