Tag: Paris Perdikaris
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Paris Perdikaris Receives New Scialog Award for Collaborative Work in Bioimaging
The Scialog: Advanced Bioimaging initiative has selected Paris Perdikaris, Assistant Professor of Mechanical Engineering and Applied Mechanics, to be part of its first cohort of researchers.
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“On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks”
Professor Paris Perdikaris, Assistant ProfessorMechanical Engineering and Applied Mechanics, has a new paper out in Computer Methods in Applied Mechanics and Engineering. Abstract: Physics-informed neural networks (PINNs) are demonstrating remarkable promise in […]
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“Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets”
The Perdikaris Group has a new paper in preprint about deep operator networks. Abstract Deep operator networks (DeepONets) are receiving increased attention thanks to their demonstrated capability to approximate nonlinear […]
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“Physics-Informed Neural Networks (PINNs) for Heat Transfer Problems”
The Perdikaris Group has a new paper out in the Journal of Heat Transfer.
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Congratulations to Professor Paris Perdikaris
Congratulations are in order for Professor Paris Perdikaris who has been selected to receive an Air Force’s Young Investigator Research Program (YIP) Award from the Air Force Office of Scientific […]
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Professor Paris Perdikaris featured in the Department of Energy’s (DOE) ASCR Discovery
In the article, “Lessons machine-learned” in the United States Department of Energy’s (DOE) ASCR Discovery, Professor Paris Perdikaris discusses using artificial intelligence algorithms to combine disparate data types in order to […]