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Machine learning for climate physics and simulations
We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …
learning (ML) and climate physics, highlighting the use of ML techniques, including …
Differentiable programming for differential equations: A review
The differentiable programming paradigm is a cornerstone of modern scientific computing. It
refers to numerical methods for computing the gradient of a numerical model's output. Many …
refers to numerical methods for computing the gradient of a numerical model's output. Many …
Universal differential equations for glacier ice flow modelling
Geoscientific models are facing increasing challenges to exploit growing datasets coming
from remote sensing. Universal Differential Equations (UDEs), aided by differentiable …
from remote sensing. Universal Differential Equations (UDEs), aided by differentiable …
Determination of 7 nitroimidazoles compounds in meat and natural casing using modified QuEChERS combined with HPLC Orbitrap MS: impact of meat processing …
ABSTRACT Validation of a High-Resolution Orbitrap (HR-Orbitrap) method for the
simultaneous identification, confirmation, and quantification of seven 5-Nitroimidazoles (5 …
simultaneous identification, confirmation, and quantification of seven 5-Nitroimidazoles (5 …
Forward and inverse modeling of ice sheet flow using physics‐informed neural networks: Application to Helheim Glacier, Greenland
Predicting the future contribution of the ice sheets to sea level rise over the next decades
presents several challenges due to a poor understanding of critical boundary conditions …
presents several challenges due to a poor understanding of critical boundary conditions …
Physics-Informed Machine Learning On Polar Ice: A Survey
The mass loss of the polar ice sheets contributes considerably to ongoing sea-level rise and
changing ocean circulation, leading to coastal flooding and risking the homes and …
changing ocean circulation, leading to coastal flooding and risking the homes and …
Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard
The ice thickness of the world's glaciers is mostly unmeasured, and physics-based models
to reconstruct ice thickness cannot always deliver accurate estimates. In this study, we use …
to reconstruct ice thickness cannot always deliver accurate estimates. In this study, we use …
Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts
Physics-informed neural networks (PINNs) commonly address ill-posed inverse problems by
uncovering unknown physics. This study presents a novel unsupervised learning framework …
uncovering unknown physics. This study presents a novel unsupervised learning framework …
Physics-aware Machine Learning for Glacier Ice Thickness Estimation: A Case Study for Svalbard
The ice thickness of the world's glaciers is mostly unmeasured and physics-based models to
reconstruct ice thickness can not always deliver accurate estimates. In this study, we use …
reconstruct ice thickness can not always deliver accurate estimates. In this study, we use …
Effect of Surface Temperature on the Distribution and Reactivity of Rh Active Sites for CO Oxidation
Single‐atom catalysts are a fast‐emerging area in which late‐transition metal atoms are
supported on oxides, metals, and carbonaceous supports. They show great promise for …
supported on oxides, metals, and carbonaceous supports. They show great promise for …