Adaptive importance sampling for seismic fragility curve estimation

• URL: http://arxiv.org/abs/2109.04323v1
• Date: Thu, 9 Sep 2021 14:56:33 GMT
• Title: Adaptive importance sampling for seismic fragility curve estimation
• Authors: Clement Gauchy, Cyril Feau, and Josselin Garnier
• Abstract summary: It is necessary to study the fragility of mechanical and civil engineered structures when subjected to seismic loads. The estimation of fragility curves relies on time-consuming numerical simulations. We propose and implement an active learning methodology based on adaptive importance sampling.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As part of Probabilistic Risk Assessment studies, it is necessary to study the fragility of mechanical and civil engineered structures when subjected to seismic loads. This risk can be measured with fragility curves, which express the probability of failure of the structure conditionally to a seismic intensity measure. The estimation of fragility curves relies on time-consuming numerical simulations, so that careful experimental design is required in order to gain the maximum information on the structure's fragility with a limited number of code evaluations. We propose and implement an active learning methodology based on adaptive importance sampling in order to reduce the variance of the training loss. The efficiency of the proposed method in terms of bias, standard deviation and prediction interval coverage are theoretically and numerically characterized.

Related papers

• Embedding generalization within the learning dynamics: An approach based-on sample path large deviation theory [0.0]
  We consider an empirical risk perturbation based learning problem that exploits methods from continuous-time perspective. We provide an estimate in the small noise limit based on the Freidlin-Wentzell theory of large deviations. We also present a computational algorithm that solves the corresponding variational problem leading to an optimal point estimates.
  arXiv  Detail & Related papers  (2024-08-04T23:31:35Z)
• Fair Risk Minimization under Causal Path-Specific Effect Constraints [3.0232957374216953]
  This paper introduces a framework for estimating fair optimal predictions using machine learning. We derive closed-form solutions for constrained optimization based on mean squared error and cross-entropy risk criteria.
  arXiv  Detail & Related papers  (2024-08-03T02:05:43Z)
• Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation [73.81105275628751]
  Finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms. We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds. Our method, Prior-Amortized Neural Posterior Estimation (PANPE), combines simulation-based inference with novel adaptive priors.
  arXiv  Detail & Related papers  (2024-07-26T10:29:16Z)
• Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification [59.81904428056924]
  We introduce SMASH: a Score MAtching estimator for learning markedPs with uncertainty quantification. Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of markedPs through score-matching. The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification.
  arXiv  Detail & Related papers  (2023-10-25T02:37:51Z)
• Conformalized Multimodal Uncertainty Regression and Reasoning [0.9205582989348333]
  This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds. We specifically discuss its application for visual odometry (VO), where environmental features such as flying domain symmetries can result in multimodal uncertainties.
  arXiv  Detail & Related papers  (2023-09-20T02:40:59Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• A Comparative Evaluation of Machine Learning Algorithms for the Prediction of R/C Buildings' Seismic Damage [0.0]
  The present paper attempts an extensive evaluation of the capability of various Machine Learning algorithms to predict the seismic response of R/C buildings. A large-scale comparison study is utilized by the most efficient Machine Learning algorithms. The experimentation shows that the LightGBM approach produces training stability, high overall performance and a remarkable coefficient of determination.
  arXiv  Detail & Related papers  (2022-03-25T05:10:14Z)
• Differential privacy and robust statistics in high dimensions [49.50869296871643]
  High-dimensional Propose-Test-Release (HPTR) builds upon three crucial components: the exponential mechanism, robust statistics, and the Propose-Test-Release mechanism. We show that HPTR nearly achieves the optimal sample complexity under several scenarios studied in the literature.
  arXiv  Detail & Related papers  (2021-11-12T06:36:40Z)
• Causality and Generalizability: Identifiability and Learning Methods [0.0]
  This thesis contributes to the research areas concerning the estimation of causal effects, causal structure learning, and distributionally robust prediction methods. We present novel and consistent linear and non-linear causal effects estimators in instrumental variable settings that employ data-dependent mean squared prediction error regularization. We propose a general framework for distributional robustness with respect to intervention-induced distributions.
  arXiv  Detail & Related papers  (2021-10-04T13:12:11Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)
• The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals [51.71066839337174]
  Existing methods can quantify the error in the target estimation, but they tend to underestimate it. We propose a new separable formulation for the estimation of a signal and of its uncertainty, avoiding the effect of overfitting. We demonstrate that the proposed method outperforms a state-of-the-art technique for signal and uncertainty estimation.
  arXiv  Detail & Related papers  (2020-11-03T12:11:27Z)

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.