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 synoptic water monitoring systems: a review of AI methods for automating water body detection and water quality monitoring using remote sensing
Water features (eg, water quantity and water quality) are one of the most important
environmental factors essential to improving climate-change resilience. Remote sensing …
environmental factors essential to improving climate-change resilience. Remote sensing …
[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 …
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 …
Assessing the physical realism of deep learning hydrologic model projections under climate change
This study examines whether deep learning models can produce reliable future projections
of streamflow under warming. We train a regional long short‐term memory network (LSTM) …
of streamflow under warming. We train a regional long short‐term memory network (LSTM) …
A review and categorization of artificial intelligence-based opportunities in wildlife, ocean and land conservation
The scholarly literature on the links between Artificial Intelligence and the United Nations'
Sustainable Development Goals is burgeoning as climate change and the biotic crisis …
Sustainable Development Goals is burgeoning as climate change and the biotic crisis …
Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?
The global decline of water quality in rivers and streams has resulted in a pressing need to
design new watershed management strategies. Water quality can be affected by multiple …
design new watershed management strategies. Water quality can be affected by multiple …
A geologically-constrained deep learning algorithm for recognizing geochemical anomalies
The effective identification of geochemical anomalies is essential in mineral exploration.
Recently, data-driven deep learning algorithms have gained popularity for recognizing the …
Recently, data-driven deep learning algorithms have gained popularity for recognizing the …
A physically constrained variational autoencoder for geochemical pattern recognition
Quantification and recognition of geochemical patterns are extremely important for
geochemical prospecting and can facilitate a better understanding of regional …
geochemical prospecting and can facilitate a better understanding of regional …
A survey of Bayesian calibration and physics-informed neural networks in scientific modeling
Computer simulations are used to model of complex physical systems. Often, these models
represent the solutions (or at least approximations) to partial differential equations that are …
represent the solutions (or at least approximations) to partial differential equations that are …