47 citations · 247 across the 20 of their papers we have counts for
7 papers · 1 filter
Variational State and Parameter Estimation
Jarrad Courts, Johannes Hendriks, Adrian Wills +2
This paper considers the problem of computing Bayesian estimates of both states and model parameters for nonlinear state-space models. Generally, this problem does not have a tract…
Deep Energy-Based NARX Models
Johannes N. Hendriks, Fredrik K. Gustafsson, Antônio H. Ribeiro +2
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data. In particular, our interest is in learning a conditional distributio…
Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics
Antônio H. Ribeiro, Johannes N. Hendriks, Adrian G. Wills +1
System identification aims to build models of dynamical systems from data. Traditionally, choosing the model requires the designer to balance between two goals of conflicting natur…
Practical Bayesian System Identification using Hamiltonian Monte Carlo
Johannes Hendriks, Adrian Wills, Brett Ninness +1
This paper considers Bayesian parameter estimation of dynamic systems using a Markov Chain Monte Carlo (MCMC) approach. The Metroplis-Hastings (MH) algorithm is employed, and the m…
Radial Basis Functions and Improved Hyperparameter Optimisation for Gaussian Process Strain Estimation
A. W. T. Gregg, J. N. Hendriks, C. M. Wensrich +1
Over the past decade, a number of algorithms for full-field elastic strain estimation from neutron and X-ray measurements have been published. Many of the recently published algori…
Bayesian Non-parametric Bragg-edge Fitting for Neutron Transmission Strain Imaging
Johannes Hendriks, Nicholas O'Dell, Adrian Wills +3
Energy resolved neutron transmission techniques can provide high-resolution images of strain within polycrystalline samples allowing the study of residual strain and stress in engi…
Linearly Constrained Neural Networks
Johannes Hendriks, Carl Jidling, Adrian Wills +1
We present a novel approach to modelling and learning vector fields from physical systems using neural networks that explicitly satisfy known linear operator constraints. To achiev…