We present a statistical learning framework for robust identification of differential equations
from noisy spatio-temporal data. We address two issues that have so far limited the …

We present an artificial neural network architecture, termed STENCIL-NET, for equation-free
forecasting of spatiotemporal dynamics from data. STENCIL-NET works by learning a …

We propose a statistical learning framework based on group-sparse regression that can be
used to (i) enforce conservation laws,(ii) ensure model equivalence, and (iii) guarantee …

Many fluid-dynamics applications require solutions in complex geometries. In these cases,
mesh generation can be a difficult and computationally expensive task for mesh-based …

We present a statistical learning framework for robust identification of partial differential
equations from noisy spatiotemporal data. Extending previous sparse regression approaches for …

Cell lineage tracing is a powerful tool for understanding how proliferation and differentiation
of individual cells contribute to population behaviour. In the developing enteric nervous …

Biologically functional DNA hairpins are found in archaea, prokaryotes and eukaryotes,
playing essential roles in various DNA transactions. However, during DNA replication, hairpin …

Modern microscopes create a data deluge with gigabytes of data generated each second,
and terabytes per day. Storing and processing this data is a severe bottleneck, not fully …

For $m,n \in \mathbb{N}$, $m\geq 1$ and a given function $f : \mathbb{R}^m\longrightarrow \mathbb{R}$,
the polynomial interpolation problem (PIP) is to determine a unisolvent node …

For $m,n \in \mathbb{N}$, $m\geq 1$ and a given function $f : \mathbb{R}^m\longrightarrow \mathbb{R}$
the polynomial interpolation problem (PIP) is to determine a \emph{generic …