A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
We present the application of a class of deep learning, known as Physics Informed Neural
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …
AI for tribology: Present and future
With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI)
can assist researchers in swiftly extracting valuable patterns, trends, and associations from …
can assist researchers in swiftly extracting valuable patterns, trends, and associations from …
A deep learning framework for solution and discovery in solid mechanics
We present the application of a class of deep learning, known as Physics Informed Neural
Networks (PINN), to learning and discovery in solid mechanics. We explain how to …
Networks (PINN), to learning and discovery in solid mechanics. We explain how to …
Machine learning and fault rupture: a review
Geophysics has historically been a data-driven field. In recent years, the intersection of
exponentially increasing amounts of data and cheap computing power, from graphics cards …
exponentially increasing amounts of data and cheap computing power, from graphics cards …
High temporal resolution prediction of street-level PM2. 5 and NOx concentrations using machine learning approach
Accurate and high temporal resolution predictions of fine particulate matter (PM 2.5) and
nitrogen oxides (NO x) concentrations are crucial for pollution control, air pollutant exposure …
nitrogen oxides (NO x) concentrations are crucial for pollution control, air pollutant exposure …
[HTML][HTML] Explainable machine learning for labquake prediction using catalog-driven features
S Karimpouli, D Caus, H Grover… - Earth and Planetary …, 2023 - Elsevier
Recently, Machine learning (ML) has been widely utilized for laboratory earthquake
(labquake) prediction using various types of data. This study pioneers in time to failure (TTF) …
(labquake) prediction using various types of data. This study pioneers in time to failure (TTF) …
Medium energy electron flux in earth's outer radiation belt (MERLIN): A machine learning model
The radiation belts of the Earth, filled with energetic electrons, comprise complex and
dynamic systems that pose a significant threat to satellite operation. While various models of …
dynamic systems that pose a significant threat to satellite operation. While various models of …
Machine learning reveals the seismic signature of eruptive behavior at Piton de la Fournaise volcano
Volcanic tremor is key to our understanding of active magmatic systems, but due to its
complexity, there is still a debate concerning its origins and how it can be used to …
complexity, there is still a debate concerning its origins and how it can be used to …
An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia
Slow slip events result from the spontaneous weakening of the subduction megathrust and
bear strong resemblance to earthquakes, only slower. This resemblance allows us to study …
bear strong resemblance to earthquakes, only slower. This resemblance allows us to study …
Machine learning bridges microslips and slip avalanches of sheared granular gouges
Understanding the origin of stress drop of fault gouges may offer deeper insights into many
geophysical processes such as earthquakes. Microslips of sheared granular gouges were …
geophysical processes such as earthquakes. Microslips of sheared granular gouges were …