Differentiable modelling to unify machine learning and physical models for geosciences
Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
Array programming with NumPy
Array programming provides a powerful, compact and expressive syntax for accessing,
manipulating and operating on data in vectors, matrices and higher-dimensional arrays …
manipulating and operating on data in vectors, matrices and higher-dimensional arrays …
Comprehensive evidence implies a higher social cost of CO2
The social cost of carbon dioxide (SC-CO2) measures the monetized value of the damages
to society caused by an incremental metric tonne of CO2 emissions and is a key metric …
to society caused by an incremental metric tonne of CO2 emissions and is a key metric …
First Sagittarius A* event horizon telescope results. IV. Variability, morphology, and black hole mass
In this paper we quantify the temporal variability and image morphology of the horizon-scale
emission from Sgr A*, as observed by the EHT in 2017 April at a wavelength of 1.3 mm. We …
emission from Sgr A*, as observed by the EHT in 2017 April at a wavelength of 1.3 mm. We …
Efficient tensor network simulation of ibm's eagle kicked ising experiment
We report an accurate and efficient classical simulation of a kicked Ising quantum system on
the heavy hexagon lattice. A simulation of this system was recently performed on a 127-qubit …
the heavy hexagon lattice. A simulation of this system was recently performed on a 127-qubit …
On neural differential equations
P Kidger - arXiv preprint arXiv:2202.02435, 2022 - arxiv.org
The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
JuMP 1.0: Recent improvements to a modeling language for mathematical optimization
JuMP is an algebraic modeling language embedded in the Julia programming language.
JuMP allows users to model optimization problems of a variety of kinds, including linear …
JuMP allows users to model optimization problems of a variety of kinds, including linear …
Pytorch: An imperative style, high-performance deep learning library
Deep learning frameworks have often focused on either usability or speed, but not both.
PyTorch is a machine learning library that shows that these two goals are in fact compatible …
PyTorch is a machine learning library that shows that these two goals are in fact compatible …
Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: a statistical analysis of publicly available case …
The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter
of Wuhan, China, has provided an opportunity to study the natural history of the recently …
of Wuhan, China, has provided an opportunity to study the natural history of the recently …
Avoiding barren plateaus using classical shadows
Variational quantum algorithms are promising algorithms for achieving quantum advantage
on near-term devices. The quantum hardware is used to implement a variational wave …
on near-term devices. The quantum hardware is used to implement a variational wave …