Outputs (2)

Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions (2019)
Journal Article
Piscopo, M. L., Spannowsky, M., & Waite, P. (2019). Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions. Physical Review D, 100(1), Article 016002. https://doi.org/10.1103/physrevd.100.016002

Starting from the observation that artificial neural networks are uniquely suited to solving optimization problems, and most physics problems can be cast as an optimization task, we introduce a novel way of finding a numerical solution to wide classe... Read More about Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions.

Adversarially-trained autoencoders for robust unsupervised new physics searches (2019)
Journal Article
Blance, A., Spannowsky, M., & Waite, P. (2019). Adversarially-trained autoencoders for robust unsupervised new physics searches. Journal of High Energy Physics, 2019(10), Article 047. https://doi.org/10.1007/jhep10%282019%29047

Machine learning techniques in particle physics are most powerful when they are trained directly on data, to avoid sensitivity to theoretical uncertainties or an underlying bias on the expected signal. To be able to train on data in searches for new... Read More about Adversarially-trained autoencoders for robust unsupervised new physics searches.