output
20172022
most citedDeepMoD: Deep learning for Model Discovery in noisy data

132 citations

15 papers

math.CO2022★ 1 cited

The -weak order and -permutahedra I: combinatorics and lattice structure

Cesar Ceballos, Viviane Pons

This is the first contribution of a sequence of papers introducing the notions of -weak order and -permutahedra, certain discrete objects that are indexed by a sequence of no…

stat.ML2021

Discovering PDEs from Multiple Experiments

Georges Tod, Gert-Jan Both, Remy Kusters

Automated model discovery of partial differential equations (PDEs) usually considers a single experiment or dataset to infer the underlying governing equations. In practice, experi…

stat.ML2021★ 1 cited

Sparsistent Model Discovery

Georges Tod, Gert-Jan Both, Remy Kusters

Discovering the partial differential equations underlying spatio-temporal datasets from very limited and highly noisy observations is of paramount interest in many scientific field…

cs.SI2020★ 18 cited

Uncovering the fragility of large-scale engineering project networks

Marc Santolini, Christos Ellinas, Christos Nicolaides

Engineering projects are notoriously hard to complete on-time, with project delays often theorised to propagate across interdependent activities. Here, we use a novel dataset consi…

physics.soc-ph2020★ 3 cited

Pseudo-Darwinian evolution of physical flows in complex networks

Geoffroy Berthelot, Liubov Tupikina, Min-Yeong Kang +2

The evolution of complex transport networks is investigated under three strategies of link removal: random, intentional attack and "Pseudo-Darwinian" strategy. At each evolution st…

cond-mat.soft2019★ 2 cited

Geometric localization in supported elastic struts

Thomas C. T. Michaels, Remy Kusters, Alexander J. Dear +3

Localized deformation patterns are a common motif in morphogenesis and are increasingly finding widespread applications in materials science, for instance as memory devices. Here w…