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 …
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights
Conventional damage detection techniques are gradually being replaced by state-of-the-art
smart monitoring and decision-making solutions. Near real-time and online damage …
smart monitoring and decision-making solutions. Near real-time and online damage …
Characterizing possible failure modes in physics-informed neural networks
Recent work in scientific machine learning has developed so-called physics-informed neural
network (PINN) models. The typical approach is to incorporate physical domain knowledge …
network (PINN) models. The typical approach is to incorporate physical domain knowledge …
A survey of uncertainty in deep neural networks
Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …
become a crucial part of various real world applications. Due to the increasing spread …
Aligning artificial intelligence with climate change mitigation
There is great interest in how the growth of artificial intelligence and machine learning may
affect global GHG emissions. However, such emissions impacts remain uncertain, owing in …
affect global GHG emissions. However, such emissions impacts remain uncertain, owing in …
Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges
The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …
the past decade, at the same time, the capability to develop high-fidelity system predictive …
A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
Physics-informed machine learning: A survey on problems, methods and applications
Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …
vision, reinforcement learning, and many scientific and engineering domains. In many real …
Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
promising transformative paradigm. ML, especially deep learning and physics-informed ML …