Interpretable machine learning: Fundamental principles and 10 grand challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
Physics-informed machine learning in prognostics and health management: State of the art and challenges
Prognostics and health management (PHM) plays a constructive role in the equipment's
entire life health service. It has long benefited from intensive research into physics modeling …
entire life health service. It has long benefited from intensive research into physics modeling …
[HTML][HTML] Understanding physics-informed neural networks: techniques, applications, trends, and challenges
Physics-informed neural networks (PINNs) represent a significant advancement at the
intersection of machine learning and physical sciences, offering a powerful framework for …
intersection of machine learning and physical sciences, offering a powerful framework for …
Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers
Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all
scientific and engineering disciplines. It has recently been shown that machine learning …
scientific and engineering disciplines. It has recently been shown that machine learning …
PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
Partial differential equations (PDEs) play a fundamental role in modeling and simulating
problems across a wide range of disciplines. Recent advances in deep learning have shown …
problems across a wide range of disciplines. Recent advances in deep learning have shown …
Augmenting physical models with deep networks for complex dynamics forecasting
Forecasting complex dynamical phenomena in settings where only partial knowledge of
their dynamics is available is a prevalent problem across various scientific fields. While …
their dynamics is available is a prevalent problem across various scientific fields. While …
When physics meets machine learning: A survey of physics-informed machine learning
Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …
of physics, which is the high level abstraction of natural phenomenons and human …
STAN: spatio-temporal attention network for pandemic prediction using real-world evidence
Objective We aim to develop a hybrid model for earlier and more accurate predictions for the
number of infected cases in pandemics by (1) using patients' claims data from different …
number of infected cases in pandemics by (1) using patients' claims data from different …
Cross-node federated graph neural network for spatio-temporal data modeling
Vast amount of data generated from networks of sensors, wearables, and the Internet of
Things (IoT) devices underscores the need for advanced modeling techniques that leverage …
Things (IoT) devices underscores the need for advanced modeling techniques that leverage …
Modeling spatio-temporal dynamical systems with neural discrete learning and levels-of-experts
In this paper, we address the issue of modeling and estimating changes in the state of the
spatio-temporal dynamical systems based on a sequence of observations like video frames …
spatio-temporal dynamical systems based on a sequence of observations like video frames …