Maziar Raissi
Maziar Raissi
Assistant Professor of Applied Mathematics, University of Colorado Boulder
Verified email at colorado.edu - Homepage
Title
Cited by
Cited by
Year
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
1646*2019
Hidden physics models: Machine learning of nonlinear partial differential equations
M Raissi, GE Karniadakis
Journal of Computational Physics 357, 125-141, 2018
5232018
Deep hidden physics models: Deep learning of nonlinear partial differential equations
M Raissi
The Journal of Machine Learning Research 19 (1), 932-955, 2018
3352018
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
M Raissi, A Yazdani, GE Karniadakis
Science 367 (6481), 1026-1030, 2020
308*2020
Machine learning of linear differential equations using Gaussian processes
M Raissi, P Perdikaris, G Karniadakis
Journal of Computational Physics 348 (Supplement C), 683 - 693, 2017
2572017
The differential effects of oil demand and supply shocks on the global economy
P Cashin, K Mohaddes, M Raissi, M Raissi
Energy Economics 44, 113-134, 2014
2202014
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
1432018
Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems
R Maziar, P Perdikaris, G Karniadakis
arXiv preprint arXiv:1801.01236, https://arxiv.org/abs/1801.01236, 2018
1422018
Inferring solutions of differential equations using noisy multi-fidelity data
M Raissi, P Perdikaris, GE Karniadakis
Journal of Computational Physics 335, 736-746, 2017
1402017
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
1362017
Deep learning of vortex-induced vibrations
M Raissi, Z Wang, MS Triantafyllou, GE Karniadakis
Journal of Fluid Mechanics 861, 119-137, 2019
1312019
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations
M Raissi
arXiv preprint arXiv:1804.07010, 2018
872018
Deep multi-fidelity Gaussian processes
M Raissi, G Karniadakis
arXiv preprint arXiv:1604.07484, 2016
382016
Machine learning of space-fractional differential equations
M Gulian, M Raissi, P Perdikaris, G Karniadakis
SIAM Journal on Scientific Computing 41 (4), A2485-A2509, 2019
342019
A deep learning framework for solution and discovery in solid mechanics
E Haghighat, M Raissi, A Moure, H Gomez, R Juanes
arXiv preprint arXiv:2003.02751, 2020
312020
Deep learning of turbulent scalar mixing
M Raissi, H Babaee, P Givi
Physical Review Fluids 4 (12), 124501, 2019
27*2019
Parametric Gaussian process regression for big data
M Raissi, H Babaee, GE Karniadakis
Computational Mechanics 64 (2), 409-416, 2019
272019
Systems biology informed deep learning for inferring parameters and hidden dynamics
A Yazdani, L Lu, M Raissi, GE Karniadakis
PLoS computational biology 16 (11), e1007575, 2020
262020
On parameter estimation approaches for predicting disease transmission through optimization, deep learning and statistical inference methods
M Raissi, N Ramezani, P Seshaiyer
Letters in Biomathematics 6 (2), 1-26, 2019
102019
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
E Haghighat, M Raissi, A Moure, H Gomez, R Juanes
Computer Methods in Applied Mechanics and Engineering 379, 113741, 2021
92021
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