2 citations · 4 across the 9 of their papers we have counts for
3 papers · 1 filter
A likelihood-based framework for simultaneously learning both noise and growth dynamics using biologically-informed neural networks
Rebecca M. Crossley, Ruth E. Baker
In recent years, neural ordinary differential equation frameworks such as Biologically-Informed Neural Networks (BINNs) have shown promise for learning mechanistic laws from sparse…
Quantifying the effect of phenotype on clustering behaviour in melanoma: from monoculture to co-culture
Nathan Schofield, Richard White, Ruth Baker +1
Melanoma is an aggressive form of skin cancer. Survival rates are excellent if it is detected early but fall markedly if it metastasises. A key step in early tumour progression is…
Learning functional components of PDEs from data using neural networks
Torkel E. Loman, Yurij Salmaniw, Antonio Leon Villares +2
Partial differential equation (PDE) models frequently contain unknown functional terms that cannot be measured directly, limiting their predictive utility. While data-driven method…