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Constructing custom thermodynamics using deep learning
Nature
One of the most exciting applications of artificial intelligence is automated scientific discovery based on previously amassed data,...
6 months ago
Physics-Informed Neural Networks: An Application-Centric Guide
Towards Data Science
When it comes to applying machine learning to physical system modeling, it is more and more common to see practitioners moving away from a...
5 months ago
On fast simulation of dynamical system with neural vector enhanced numerical solver | Scientific Reports
Nature
The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines.
9 months ago
Discovering Differential Equations with Physics-Informed Neural Networks and Symbolic Regression
Towards Data Science
Differential equations serve as a powerful framework to capture and understand the dynamic behaviors of physical systems. By describing how...
11 months ago
State estimation of a physical system with unknown governing equations
Nature
State estimation is concerned with reconciling noisy observations of a physical system with the mathematical model believed to predict its...
8 months ago
Predicting network dynamics without requiring the knowledge of the interaction graph
PNAS
SignificanceDynamics on networks describe a plethora of physical phenomena, including the viral spread on contact networks, the competition between species...
20 months ago
Deep multi-modal learning for joint linear representation of nonlinear dynamical systems | Scientific Reports
Nature
Dynamical systems pervasively seen in most real-life applications are complex and behave by following certain evolution rules or dynamical...
23 months ago
A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks | Scientific Reports
Nature
Li-ion batteries are the main power source used in electric propulsion applications (e.g., electric cars, unmanned aerial vehicles,...
10 months ago
Symplectic encoders for physics-constrained variational dynamics inference | Scientific Reports
Nature
We propose a new variational autoencoder (VAE) with physical constraints capable of learning the dynamics of Multiple Degree of Freedom...
16 months ago
Unsupervised learning of aging principles from longitudinal data
Nature
Age is the leading risk factor for prevalent diseases and death. However, the relation between age-related physiological changes and...
20 months ago