Gaussian Process Surrogate Models for Efficient Estimation of Structural Response Distributions and Order Statistics

• URL: http://arxiv.org/abs/2503.01242v1
• Date: Mon, 03 Mar 2025 07:12:32 GMT
• Title: Gaussian Process Surrogate Models for Efficient Estimation of Structural Response Distributions and Order Statistics
• Authors: Vegard Flovik, Sebastian Winter, Christian Agrell,
• Abstract summary: We propose an approach using Gaussian Process (GP) surrogate models trained on a limited set of simulation outputs to directly generate the structural response distribution.<n>Our results indicate that the GP surrogate models provide comparable results to full simulations but at a fraction of the computational cost.
• Score: 1.1470070927586016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Engineering disciplines often rely on extensive simulations to ensure that structures are designed to withstand harsh conditions while avoiding over-engineering for unlikely scenarios. Assessments such as Serviceability Limit State (SLS) involve evaluating weather events, including estimating loads not expected to be exceeded more than a specified number of times (e.g., 100) throughout the structure's design lifetime. Although physics-based simulations provide robust and detailed insights, they are computationally expensive, making it challenging to generate statistically valid representations of a wide range of weather conditions. To address these challenges, we propose an approach using Gaussian Process (GP) surrogate models trained on a limited set of simulation outputs to directly generate the structural response distribution. We apply this method to an SLS assessment for estimating the order statistics \(Y_{100}\), representing the 100th highest response, of a structure exposed to 25 years of historical weather observations. Our results indicate that the GP surrogate models provide comparable results to full simulations but at a fraction of the computational cost.

Related papers

• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Constrained Posterior Sampling: Time Series Generation with Hard Constraints [21.48057675740321]
  Time series samples are crucial for stress-testing models and protecting user privacy by using synthetic data. Existing approaches for generating constrained time series are either not scalable or degrade sample quality. We introduce Constrained Posterior Sampling (CPS), a diffusion-based sampling algorithm that aims to project the posterior mean estimate into the constraint set after each denoising update. CPS outperforms state-of-the-art methods in sample quality and similarity to real time series by around 10% and 42%, respectively, on real-world stocks, traffic, and air quality datasets.
  arXiv  Detail & Related papers  (2024-10-16T15:16:04Z)
• A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data [12.566163525039558]
  We present a strategy for training neural network models to non-intrusively correct under-resolved long-time simulations of chaotic systems. We demonstrate its ability to accurately predict the anisotropic statistics over time horizons more than 30 times longer than the data seen in training.
  arXiv  Detail & Related papers  (2024-08-02T18:34:30Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking [65.24988062003096]
  We present NAVSIM, a framework for benchmarking vision-based driving policies. Our simulation is non-reactive, i.e., the evaluated policy and environment do not influence each other. NAVSIM enabled a new competition held at CVPR 2024, where 143 teams submitted 463 entries, resulting in several new insights.
  arXiv  Detail & Related papers  (2024-06-21T17:59:02Z)
• IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark [8.960138649105156]
  We propose a strategy for turning observational data into sequential structural causal models. We apply these ideas to the well-known Adult income data set to construct the IncomeSCM simulator. The tasks present a suitable challenge, with effect estimates varying greatly in quality between methods.
  arXiv  Detail & Related papers  (2024-05-25T05:40:16Z)
• Optimising Highly-Parallel Simulation-Based Verification of Cyber-Physical Systems [0.0]
  Cyber-Physical Systems (CPSs) arise in many industry-relevant domains and are often mission- or safety-critical. System-Level Verification (SLV) of CPSs aims at certifying that given (e.g. safety or liveness) specifications are met or at estimating the value of some.
  arXiv  Detail & Related papers  (2023-07-28T08:08:27Z)
• Uncertainty-aware Remaining Useful Life predictor [57.74855412811814]
  Remaining Useful Life (RUL) estimation is the problem of inferring how long a certain industrial asset can be expected to operate. In this work, we consider Deep Gaussian Processes (DGPs) as possible solutions to the aforementioned limitations. The performance of the algorithms is evaluated on the N-CMAPSS dataset from NASA for aircraft engines.
  arXiv  Detail & Related papers  (2021-04-08T08:50:44Z)
• Reinforcement Learning for Adaptive Mesh Refinement [63.7867809197671]
  We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning to train refinement policies directly from simulation. The model sizes of these policy architectures are independent of the mesh size and hence scale to arbitrarily large and complex simulations.
  arXiv  Detail & Related papers  (2021-03-01T22:55:48Z)
• A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation [8.808993671472349]
  We propose a framework that integrates classical Monte Carlo simulators and Wasserstein generative adversarial networks to model, estimate, and simulate a broad class of arrival processes. Classical Monte Carlo simulators have advantages at capturing interpretable "physics" of a Poisson object, whereas neural-network-based simulators have advantages at capturing less-interpretable complicated dependence within a high-dimensional distribution.
  arXiv  Detail & Related papers  (2020-12-27T13:32:16Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)
• Fast Bayesian Estimation of Spatial Count Data Models [0.0]
  We introduce Variational Bayes (VB) as an optimisation problem instead of a simulation problem. A VB method is derived for posterior inference in negative binomial models with unobserved parameter and spatial dependence. The VB approach is around 45 to 50 times faster than MCMC on a regular eight-core processor in a simulation and an empirical study.
  arXiv  Detail & Related papers  (2020-07-07T10:24:45Z)

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.