32 citations · 91 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…