Paris Perdikaris
Paris Perdikaris
Assistant Professor, University of Pennsylvania
Adresse e-mail validée de seas.upenn.edu
Titre
Citée par
Citée par
Année
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M Raissi, P Perdikaris, GE Karniadakis
Journal of Computational Physics 378, 686-707, 2019
10212019
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M Raissi, P Perdikaris, GE Karniadakis
Journal of Computational Physics 378, 686-707, 2019
10212019
Physics informed deep learning (part ii): Data-driven discovery of nonlinear partial differential equations
M Raissi, P Perdikaris, GE Karniadakis
arXiv preprint arXiv:1711.10566, 2017
362*2017
Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations
M Raissi, P Perdikaris, GE Karniadakis
arXiv preprint arXiv:1711.10561, 2017
3622017
Machine learning of linear differential equations using Gaussian processes
M Raissi, P Perdikaris, G Karniadakis
Journal of Computational Physics 348, 683-693, 2017
2232017
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Y Zhu, N Zabaras, PS Koutsourelakis, P Perdikaris
Journal of Computational Physics 394, 56-81, 2019
2052019
Multistep neural networks for data-driven discovery of nonlinear dynamical systems
M Raissi, P Perdikaris, GE Karniadakis
arXiv preprint arXiv:1801.01236, 2018
1292018
Multi-fidelity modelling via recursive co-kriging and Gaussian–Markov random fields
P Perdikaris, D Venturi, JO Royset, GE Karniadakis
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015
1262015
Inferring solutions of differential equations using noisy multi-fidelity data
M Raissi, P Perdikaris, GE Karniadakis
Journal of Computational Physics 335, 736-746, 2017
1252017
Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling
P Perdikaris, M Raissi, A Damianou, ND Lawrence, GE Karniadakis
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2017
1192017
Numerical Gaussian processes for time-dependent and nonlinear partial differential equations
M Raissi, P Perdikaris, GE Karniadakis
SIAM Journal on Scientific Computing 40 (1), A172-A198, 2018
1182018
Adversarial uncertainty quantification in physics-informed neural networks
Y Yang, P Perdikaris
Journal of Computational Physics 394, 136-152, 2019
952019
Fractional-order viscoelasticity in one-dimensional blood flow models
P Perdikaris, GE Karniadakis
Annals of biomedical engineering 42 (5), 1012-1023, 2014
862014
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
M Alber, AB Tepole, WR Cannon, S De, S Dura-Bernal, K Garikipati, ...
NPJ digital medicine 2 (1), 1-11, 2019
762019
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
G Kissas, Y Yang, E Hwuang, WR Witschey, JA Detre, P Perdikaris
Computer Methods in Applied Mechanics and Engineering 358, 112623, 2020
71*2020
Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond
P Perdikaris, GE Karniadakis
Journal of The Royal Society Interface 13 (118), 20151107, 2016
642016
Multi-fidelity Gaussian process regression for prediction of random fields
L Parussini, D Venturi, P Perdikaris, GE Karniadakis
Journal of Computational Physics 336, 36-50, 2017
602017
Fractional modeling of viscoelasticity in 3D cerebral arteries and aneurysms
Y Yu, P Perdikaris, GE Karniadakis
Journal of computational physics 323, 219-242, 2016
572016
Multifidelity information fusion algorithms for high-dimensional systems and massive data sets
P Perdikaris, D Venturi, GE Karniadakis
SIAM Journal on Scientific Computing 38 (4), B521-B538, 2016
512016
An effective fractal-tree closure model for simulating blood flow in large arterial networks
P Perdikaris, L Grinberg, GE Karniadakis
Annals of biomedical engineering 43 (6), 1432-1442, 2015
502015
Le système ne peut pas réaliser cette opération maintenant. Veuillez réessayer plus tard.
Articles 1–20