Search Results for author: Georgios Kissas

Found 4 papers, 3 papers with code

NOMAD: Nonlinear Manifold Decoders for Operator Learning

no code implementations7 Jun 2022 Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas

Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling.

Operator learning

Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors

1 code implementation6 Mar 2022 Yibo Yang, Georgios Kissas, Paris Perdikaris

Finally, we provide an optimized JAX library called {\em UQDeepONet} that can accommodate large model architectures, large ensemble sizes, as well as large data-sets with excellent parallel performance on accelerated hardware, thereby enabling uncertainty quantification for DeepONets in realistic large-scale applications.

Learning Operators with Coupled Attention

1 code implementation4 Jan 2022 Georgios Kissas, Jacob Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris

Supervised operator learning is an emerging machine learning paradigm with applications to modeling the evolution of spatio-temporal dynamical systems and approximating general black-box relationships between functional data.

Operator learning

Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

1 code implementation13 May 2019 Georgios Kissas, Yibo Yang, Eileen Hwuang, Walter R. Witschey, John A. Detre, Paris Perdikaris

Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networks and return detailed predictions on flow patterns, wall shear stresses, and pulse wave propagation.

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