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stat.ML2025
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…
stat.ML2024
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…
stat.ML2024
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…