ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2504.13320
44
0

Gradient-Free Sequential Bayesian Experimental Design via Interacting Particle Systems

17 April 2025
Robert Gruhlke
Matei Hanu
Claudia Schillings
Philipp Wacker
    BDL
ArXivPDFHTML
Abstract

We introduce a gradient-free framework for Bayesian Optimal Experimental Design (BOED) in sequential settings, aimed at complex systems where gradient information is unavailable. Our method combines Ensemble Kalman Inversion (EKI) for design optimization with the Affine-Invariant Langevin Dynamics (ALDI) sampler for efficient posterior sampling-both of which are derivative-free and ensemble-based. To address the computational challenges posed by nested expectations in BOED, we propose variational Gaussian and parametrized Laplace approximations that provide tractable upper and lower bounds on the Expected Information Gain (EIG). These approximations enable scalable utility estimation in high-dimensional spaces and PDE-constrained inverse problems. We demonstrate the performance of our framework through numerical experiments ranging from linear Gaussian models to PDE-based inference tasks, highlighting the method's robustness, accuracy, and efficiency in information-driven experimental design.

View on arXiv
@article{gruhlke2025_2504.13320,
  title={ Gradient-Free Sequential Bayesian Experimental Design via Interacting Particle Systems },
  author={ Robert Gruhlke and Matei Hanu and Claudia Schillings and Philipp Wacker },
  journal={arXiv preprint arXiv:2504.13320},
  year={ 2025 }
}
Comments on this paper