Maziar Raissi
Maziar Raissi
Assistant Professor of Applied Mathematics (Research), Division of Applied Mathematics, Brown
Verified email at brown.edu - Homepage
TitleCited byYear
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
207*2019
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
1772014
Hidden physics models: Machine learning of nonlinear partial differential equations
M Raissi, GE Karniadakis
Journal of Computational Physics 357, 125-141, 2018
1522018
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
1002017
Deep hidden physics models: Deep learning of nonlinear partial differential equations
M Raissi
The Journal of Machine Learning Research 19 (1), 932-955, 2018
692018
Inferring solutions of differential equations using noisy multi-fidelity data
M Raissi, P Perdikaris, GE Karniadakis
Journal of Computational Physics 335, 736-746, 2017
642017
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
572018
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
512018
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
512017
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations
M Raissi
arXiv preprint arXiv:1804.07010, 2018
272018
Hidden fluid mechanics: A navier-stokes informed deep learning framework for assimilating flow visualization data
M Raissi, A Yazdani, GE Karniadakis
arXiv preprint arXiv:1808.04327, 2018
222018
Parametric Gaussian process regression for big data
M Raissi, H Babaee, GE Karniadakis
Computational Mechanics 64 (2), 409-416, 2019
182019
Deep learning of vortex-induced vibrations
M Raissi, Z Wang, MS Triantafyllou, GE Karniadakis
Journal of Fluid Mechanics 861, 119-137, 2019
172019
Deep multi-fidelity Gaussian processes
M Raissi, G Karniadakis
arXiv preprint arXiv:1604.07484, 2016
152016
Application of local improvements to reduced-order models to sampling methods for nonlinear PDEs with noise
M Raissi, P Seshaiyer
International Journal of Computer Mathematics 95 (5), 870-880, 2018
7*2018
A multi-fidelity stochastic collocation method for parabolic partial differential equations with random input data
M Raissi, P Seshaiyer
International Journal for Uncertainty Quantification 4 (3), 2014
7*2014
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
52019
Muti-fidelity Stochastic Collocation methods using Model Reduction techniques
M Raissi
12013
A deep-learning framework for inference in geomechanics
E Haghighat, M Raissi, AM Rosende, H Gomez, R Juanes
AGU Fall Meeting 2019, 2019
2019
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, 1-26, 2019
2019
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Articles 1–20