47 citations · 294 across the 31 of their papers we have counts for
37 papers
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method
Anna Wigren, Johan Wågberg, Fredrik Lindsten +2
Identification of nonlinear systems is a challenging problem. Physical knowledge of the system can be used in the identification process to significantly improve the predictive per…
An Improved Random Matrix Prediction Model for Manoeuvring Extended Targets
Nathan J. Bartlett, Chris Renton, Adrian G. Wills
This paper proposes an improved prediction update for extended target tracking with the random matrix model. A key innovation is to employ a generalised non-central inverse Wishart…
Data to Controller for Nonlinear Systems: An Approximate Solution
Johannes N. Hendriks, James R. Z. Holdsworth, Adrian G. Wills +2
This paper considers the problem of determining an optimal control action based on observed data. We formulate the problem assuming that the system can be modelled by a nonlinear s…
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Filip de Roos, Carl Jidling, Adrian Wills +2
Machine learning practitioners invest significant manual and computational resources in finding suitable learning rates for optimization algorithms. We provide a probabilistic moti…
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…
Variational System Identification for Nonlinear State-Space Models
Jarrad Courts, Adrian Wills, Thomas Schön +1
This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem. We address this challenge by employing a variational infe…
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…
A Heteroscedastic Likelihood Model for Two-frame Optical Flow
Timothy Farnworth, Christopher Renton, Reuben Strydom +2
Machine vision is an important sensing technology used in mobile robotic systems. Advancing the autonomy of such systems requires accurate characterisation of sensor uncertainty. V…
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…
A Variational Expectation-Maximisation Algorithm for Learning Jump Markov Linear Systems
Mark P. Balenzuela, Adrian G. Wills, Christopher Renton +1
Jump Markov linear systems (JMLS) are a useful class which can be used to model processes which exhibit random changes in behavior during operation. This paper presents a numerical…
A New Smoothing Algorithm for Jump Markov Linear Systems
Mark P. Balenzuela, Adrian G. Wills, Christopher Renton +1
This paper presents a method for calculating the smoothed state distribution for Jump Markov Linear Systems. More specifically, the paper details a novel two-filter smoother that p…
Bayesian Parameter Identification for Jump Markov Linear Systems
Mark P. Balenzuela, Adrian G. Wills, Christopher Renton +1
This paper presents a Bayesian method for identification of jump Markov linear system parameters. A primary motivation is to provide accurate quantification of parameter uncertaint…
Gaussian Variational State Estimation for Nonlinear State-Space Models
Jarrad Courts, Adrian Wills, Thomas B. Schön
In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered. Due to the nonlinear nature of the m…
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…
Learning Continuous Occupancy Maps with the Ising Process Model
Nicholas O'Dell, Christopher Renton, Adrian Wills
We present a new method of learning a continuous occupancy field for use in robot navigation. Occupancy grid maps, or variants of, are possibly the most widely used and accepted me…
Deep kernel learning for integral measurements
Carl Jidling, Johannes Hendriks, Thomas B. Schön +1
Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions. We present a method that makes this approach feas…
Stochastic quasi-Newton with line-search regularization
Adrian Wills, Thomas Schön
In this paper we present a novel quasi-Newton algorithm for use in stochastic optimisation. Quasi-Newton methods have had an enormous impact on deterministic optimisation problems…
Neutron Diffraction Strain Tomography: Demonstration and Proof-of-Concept
A. W. T. Gregg, J. N. Hendriks, C. M. Wensrich +2
Recently, a number of reconstruction algorithms have been presented for residual strain tomography from Bragg-edge neutron transmission measurements. In this paper, we examine whet…
Tomographic Reconstruction of Triaxial Strain Fields from Bragg-Edge Neutron Imaging
J. N. Hendriks, A. W. T. Gregg, R. R. Jackson +6
This paper presents a proof-of-concept demonstration of triaxial strain tomography from Bragg-edge neutron imaging within a three-dimensional sample. Bragg-edge neutron transmissio…
Neutron Transmission Strain Tomography for Non-Constant Stress-Free Lattice Spacing
J. N. Hendriks, C. Jidling, T. B. Schön +3
Recently, several algorithms for strain tomography from energy-resolved neutron transmission measurements have been proposed. These methods assume that the stress-free lattice spac…
A Bayesian Approach to Triaxial Strain Tomography from High-energy X-ray Diffraction
J. N. Hendriks, C. M. Wensrich, A. Wills
Diffraction of high-energy X-rays produced at synchrotron sources can provide rapid strain measurements, with high spatial resolution, and good penetrating power. With an uncollima…
Evaluating the squared-exponential covariance function in Gaussian processes with integral observations
J. N. Hendriks, C. Jidling, A. Wills +1
This paper deals with the evaluation of double line integrals of the squared exponential covariance function. We propose a new approach in which the double integral is reduced to a…