Physics-informed machine learning
Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
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 …
[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-informed dynamic mode decomposition
PJ Baddoo, B Herrmann… - … of the Royal …, 2023 - royalsocietypublishing.org
In this work, we demonstrate how physical principles—such as symmetries, invariances and
conservation laws—can be integrated into the dynamic mode decomposition (DMD). DMD is …
conservation laws—can be integrated into the dynamic mode decomposition (DMD). DMD is …
Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering
Accurately predicting the remaining useful life (RUL) of lithium-ion rechargeable batteries
remains challenging as the battery capacity degrades in a stochastic manner given the …
remains challenging as the battery capacity degrades in a stochastic manner given the …
Knowledge-augmented deep learning and its applications: A survey
Deep learning models, though having achieved great success in many different fields over
the past years, are usually data-hungry, fail to perform well on unseen samples, and lack …
the past years, are usually data-hungry, fail to perform well on unseen samples, and lack …
A survey on physics informed reinforcement learning: Review and open problems
The inclusion of physical information in machine learning frameworks has revolutionized
many application areas. This involves enhancing the learning process by incorporating …
many application areas. This involves enhancing the learning process by incorporating …
A deep learning framework for design and analysis of surgical bioprosthetic heart valves
A Balu, S Nallagonda, F Xu, A Krishnamurthy… - Scientific reports, 2019 - nature.com
Bioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they
are prone to fatigue failure; estimating their remaining life directly from medical images is …
are prone to fatigue failure; estimating their remaining life directly from medical images is …
Interpretable deep learning for guided microstructure-property explorations in photovoltaics
The microstructure determines the photovoltaic performance of a thin film organic
semiconductor film. The relationship between microstructure and performance is usually …
semiconductor film. The relationship between microstructure and performance is usually …
Algorithmically-consistent deep learning frameworks for structural topology optimization
Topology optimization has emerged as a popular approach to refine a component's design
and increase its performance. However, current state-of-the-art topology optimization …
and increase its performance. However, current state-of-the-art topology optimization …