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Found 9 papers, 3 papers with code
Score Normalization for a Faster Diffusion Exponential Integrator Sampler
As a simple fix, we propose to instead reparameterise the score (at inference) by dividing it by the average absolute value of previous score estimates at that time step collected from offline high NFE generations.
Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling
In this paper, we propose a diffusion model based channel sampling approach for rapidly synthesizing channel realizations from limited data.
Bayesian Inference in Physics-Based Nonlinear Flame Models
The ensemble learns a surrogate of the approximate Bayesian posterior of the parameters given the observations, from which the flame can be re-simulated beyond the observation window of the experiment.
Simultaneous boundary shape estimation and velocity field de-noising in Magnetic Resonance Velocimetry using Physics-informed Neural Networks
In this paper, we present a physics-informed neural network that instead uses the noisy MRV data alone to simultaneously infer the most likely boundary shape and de-noised velocity field.
Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning
The 100 MW cryogenic liquid oxygen/hydrogen multi-injector combustor BKD operated by the DLR Institute of Space Propulsion is a research platform that allows the study of thermoacoustic instabilities under realistic conditions, representative of small upper stage rocket engines.
Online parameter inference for the simulation of a Bunsen flame using heteroscedastic Bayesian neural network ensembles
Heteroscedastic Bayesian neural network ensembles are trained on a library of 1. 7 million flame fronts simulated in LSGEN2D, a G-equation solver, to learn the Bayesian posterior distribution of the model parameters given observations.
Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning
In most cases, the method is able to timely predict two types of thermoacoustic instabilities on test data not used for training.
Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles
The trained neural networks are then used to infer model parameters from real videos of a premixed Bunsen flame captured using a high-speed camera in our lab.
Ensembling geophysical models with Bayesian Neural Networks
Ensembles of geophysical models improve projection accuracy and express uncertainties.
