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Neural operators for accelerating scientific simulations and design
Scientific discovery and engineering design are currently limited by the time and cost of
physical experiments. Numerical simulations are an alternative approach but are usually …
physical experiments. Numerical simulations are an alternative approach but are usually …
Pushing the frontiers in climate modelling and analysis with machine learning
Climate modelling and analysis are facing new demands to enhance projections and
climate information. Here we argue that now is the time to push the frontiers of machine …
climate information. Here we argue that now is the time to push the frontiers of machine …
Fourier neural operator for parametric partial differential equations
Z Li, N Kovachki, K Azizzadenesheli, B Liu… - ar**s
between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural …
between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural …
Learning continuous image representation with local implicit image function
How to represent an image? While the visual world is presented in a continuous manner,
machines store and see the images in a discrete way with 2D arrays of pixels. In this paper …
machines store and see the images in a discrete way with 2D arrays of pixels. In this paper …
Super-resolution analysis via machine learning: a survey for fluid flows
This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …
Physics-informed machine learning: case studies for weather and climate modelling
Machine learning (ML) provides novel and powerful ways of accurately and efficiently
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …
A review of physics-informed machine learning in fluid mechanics
Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …
with machine learning (ML) algorithms, which results in higher data efficiency and more …
Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …