1 citations · 1 across the 4 of their papers we have counts for
7 papers · 1 filter
Online Bayesian Experimental Design for Partially Observed Dynamical Systems
Sara Pérez-Vieites, Sahel Iqbal, Simo Särkkä +1
Bayesian experimental design (BED) provides a principled framework for optimizing data collection by choosing experiments that are maximally informative about unknown parameters. H…
Dual-Level Models for Physics-Informed Multi-Step Time Series Forecasting
Mahdi Nasiri, Johanna Kortelainen, Simo Särkkä
This paper develops an approach for multi-step forecasting of dynamical systems by integrating probabilistic input forecasting with physics-informed output prediction. Accurate mul…
Conditional Normalizing Flow Surrogate for Monte Carlo Prediction of Radiative Properties in Nanoparticle-Embedded Layers
Fahime Seyedheydari, Kevin Conley, Simo Särkkä
We present a probabilistic, data-driven surrogate model for predicting the radiative properties of nanoparticle embedded scattering media. The model uses conditional normalizing fl…
Determination of Particle-Size Distributions from Light-Scattering Measurement Using Constrained Gaussian Process Regression
Fahime Seyedheydari, Mahdi Nasiri, Marcin MiÅkowski +1
In this work, we propose a novel methodology for robustly estimating particle size distributions from optical scattering measurements using constrained Gaussian process regression.…
Conditioning diffusion models by explicit forward-backward bridging
Adrien Corenflos, Zheng Zhao, Simo Särkkä +2
Given an unconditional diffusion model targeting a joint model , using it to perform conditional simulation is still largely an open question and is typica…
Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design
Sahel Iqbal, Hany Abdulsamad, Sara Pérez-Vieites +2
This paper introduces the Inside-Out Nested Particle Filter (IO-NPF), a novel, fully recursive, algorithm for amortized sequential Bayesian experimental design in the non-exchangea…