Can deep learning beat numerical weather prediction?
The recent hype about artificial intelligence has sparked renewed interest in applying the
successful deep learning (DL) methods for image recognition, speech recognition, robotics …
successful deep learning (DL) methods for image recognition, speech recognition, robotics …
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …
Digital twin: Values, challenges and enablers from a modeling perspective
Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
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 …
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 …
Towards physics-informed deep learning for turbulent flow prediction
While deep learning has shown tremendous success in a wide range of domains, it remains
a grand challenge to incorporate physical principles in a systematic manner to the design …
a grand challenge to incorporate physical principles in a systematic manner to the design …
Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales
Recent observations with varied schedules and types (moving average, snapshot, or
regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate …
regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate …
Enforcing analytic constraints in neural networks emulating physical systems
Neural networks can emulate nonlinear physical systems with high accuracy, yet they may
produce physically inconsistent results when violating fundamental constraints. Here, we …
produce physically inconsistent results when violating fundamental constraints. Here, we …
Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus
A multitude of cyber-physical system (CPS) applications, including design, control,
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …
Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles
Physics-based models are often used to study engineering and environmental systems. The
ability to model these systems is the key to achieving our future environmental sustainability …
ability to model these systems is the key to achieving our future environmental sustainability …