32 citations · 91 across the 11 of their papers we have counts for
17 papers
Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations
Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström +2
In this paper, we propose Lagrangian Gaussian Processes (LGPs) for probabilistic and data-efficient learning of dynamics via discrete forced Euler-Lagrange equations. Importantly,…
Simultaneous State Estimation and Online Model Learning in a Soft Robotic System
Jan-Hendrik Ewering, Max Bartholdt, Simon F. G. Ehlers +3
Operating complex real-world systems, such as soft robots, can benefit from precise predictive control schemes that require accurate state and model knowledge. This knowledge is ty…
Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
Jan-Hendrik Ewering, Robin E. Herrmann, Niklas Wahlström +2
Embedding non-restrictive prior knowledge, such as energy conservation laws, into learning methods is a key motive to construct physically consistent dynamics models from limited d…
Repulsive Ensembles for Bayesian Inference in Physics-informed Neural Networks
Philipp Pilar, Markus Heinonen, Niklas Wahlström
Physics-informed neural networks (PINNs) have proven an effective tool for solving differential equations, in particular when considering non-standard or ill-posed settings. When i…
Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks
Philipp Pilar, Niklas Wahlström
Generative adversarial networks constitute a powerful approach to generative modeling. While generated samples often are indistinguishable from real data, there is no guarantee tha…
Invertible Kernel PCA with Random Fourier Features
Daniel Gedon, Antôni H. Ribeiro, Niklas Wahlström +1
Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation. The prevailing method to…
Physics-informed Neural Networks with Unknown Measurement Noise
Philipp Pilar, Niklas Wahlström
Physics-informed neural networks (PINNs) constitute a flexible approach to both finding solutions and identifying parameters of partial differential equations. Most works on the to…
Incorporating Sum Constraints into Multitask Gaussian Processes
Philipp Pilar, Carl Jidling, Thomas B. Schön +1
Machine learning models can be improved by adapting them to respect existing background knowledge. In this paper we consider multitask Gaussian processes, with background knowledge…
Learning deep autoregressive models for hierarchical data
Carl R. Andersson, Niklas Wahlström, Thomas B. Schön
We propose a model for hierarchical structured data as an extension to the stochastic temporal convolutional network. The proposed model combines an autoregressive model with a hie…
Deep State Space Models for Nonlinear System Identification
Daniel Gedon, Niklas Wahlström, Thomas B. Schön +1
Deep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs. Th…
Deep Convolutional Networks in System Identification
Carl Andersson, Antônio H. Ribeiro, Koen Tiels +2
Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system…
Probabilistic approach to limited-data computed tomography reconstruction
Zenith Purisha, Carl Jidling, Niklas Wahlström +2
In this work, we consider the inverse problem of reconstructing the internal structure of an object from limited x-ray projections. We use a Gaussian process prior to model the tar…
Data-Driven Impulse Response Regularization via Deep Learning
Carl Andersson, Niklas Wahlström, Thomas B. Schön
We consider the problem of impulse response estimation of stable linear single-input single-output systems. It is a well-studied problem where flexible non-parametric models recent…
Linearly constrained Gaussian processes
Carl Jidling, Niklas Wahlström, Adrian Wills +1
We consider a modification of the covariance function in Gaussian processes to correctly account for known linear constraints. By modelling the target function as a transformation…
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön +1
Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems…
From Pixels to Torques: Policy Learning with Deep Dynamical Models
Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth
Data-efficient learning in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. In this paper, we…
Learning deep dynamical models from image pixels
Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth
Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only ava…