186 citations · 241 across the 7 of their papers we have counts for
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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 the inverse problem. When inferring soluti…
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…