mailitics

Category: cs.CE

  • Distributional Computational Graphs: Error Bounds

    Distributional Computational Graphs: Error Bounds arXiv:2601.16250v1 Announce Type: new Abstract: We study a general framework of distributional computational graphs: computational graphs whose inputs are probability distributions rather than point values. We analyze the discretization error that arises when these graphs are evaluated using finite approximations of continuous probability distributions. Such an approximation might be the…

  • Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference

    Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference arXiv:2601.00038v1 Announce Type: new Abstract: This work develops an active learning framework to intelligently enrich data-driven reduced-order models (ROMs) of parametric dynamical systems, which can serve as the foundation of virtual assets in a digital twin. Data-driven ROMs are explainable, computationally…

  • A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications

    A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications arXiv:2511.00366v1 Announce Type: new Abstract: Digital twins are developed to model the behavior of a specific physical asset (or twin), and they can consist of high-fidelity physics-based models or surrogates. A highly accurate surrogate is often preferred over multi-physics models as…

  • Projection-based multifidelity linear regression for data-scarce applications

    Projection-based multifidelity linear regression for data-scarce applications arXiv:2508.08517v1 Announce Type: new Abstract: Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifidelity methods for multiple-input multiple-output linear regression targeting data-limited applications with high-dimensional outputs. Multifidelity methods integrate many inexpensive low-fidelity model evaluations…

  • Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators

    Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators arXiv:2507.14652v1 Announce Type: new Abstract: Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s…

  • On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions

    On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions arXiv:2506.11683v1 Announce Type: new Abstract: Solving inverse problems in cardiovascular modeling is particularly challenging due to the high computational cost of running high-fidelity simulations. In this work, we focus on Bayesian parameter estimation and explore different methods to reduce the…

  • Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate

    Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate arXiv:2412.11257v1 Announce Type: new Abstract: Despite being an essential tool across engineering and finance, Monte Carlo simulation can be computationally intensive, especially in large-scale, path-dependent problems that hinder straightforward parallelization. A natural alternative is to replace simulation with machine learning or surrogate prediction, though this…