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Maziar Raissi

Orcid: 0000-0002-8467-4568

According to our database¹, Maziar Raissi authored at least 28 papers between 2016 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

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In proceedings

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PhD thesis

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Links

On csauthors.net:

Bibliography

2023

PINNs-TF2: Fast and User-Friendly Physics-Informed Neural Networks in TensorFlow V2.

[BibT_eX]

[DOI]

Reza Akbarian Bafghi

,

CoRR, 2023

A Survey on Physics Informed Reinforcement Learning: Review and Open Problems.

[BibT_eX]

[DOI]

Chayan Banerjee

,

,

,

CoRR, 2023

Real Estate Property Valuation using Self-Supervised Vision Transformers.

[BibT_eX]

[DOI]

Mahdieh Yazdani

,

CoRR, 2023

Temporal Consistency Loss for Physics-Informed Neural Networks.

[BibT_eX]

[DOI]

,

,

,

Arezoo M. Ardekani

CoRR, 2023

Open Problems in Applied Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2023

2022

Scientific Machine Learning Through Physics-Informed Neural Networks: Where we are and What's Next.

[BibT_eX]

[DOI]

Salvatore Cuomo

,

Vincenzo Schiano Di Cola

,

Fabio Giampaolo

,

Gianluigi Rozza

,

,

Francesco Piccialli

J. Sci. Comput., 2022

2021

Disease Informed Neural Networks.

[BibT_eX]

[DOI]

,

CoRR, 2021

Call for Special Issue Papers: Big Scientific Data and Machine Learning in Science and Engineering: Deadline for Manuscript Submission: February 1, 2022.

[BibT_eX]

[DOI]

Farhad Pourkamali-Anaraki

,

Mohammad Amin Hariri-Ardebili

,

,

Big Data, 2021

2020

Systems biology informed deep learning for inferring parameters and hidden dynamics.

[BibT_eX]

[DOI]

Alireza Yazdani

,

,

,

George Em Karniadakis

PLoS Comput. Biol., 2020

A deep learning framework for solution and discovery in solid mechanics: linear elasticity.

[BibT_eX]

[DOI]

Ehsan Haghighat

,

,

,

,

CoRR, 2020

2019

Machine Learning of Space-Fractional Differential Equations.

[BibT_eX]

[DOI]

Mamikon A. Gulian

,

,

Paris Perdikaris

,

George E. Karniadakis

SIAM J. Sci. Comput., 2019

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

[BibT_eX]

[DOI]

,

Paris Perdikaris

,

George E. Karniadakis

J. Comput. Phys., 2019

2018

Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations.

[BibT_eX]

[DOI]

,

Paris Perdikaris

,

George E. Karniadakis

SIAM J. Sci. Comput., 2018

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2018

Hidden physics models: Machine learning of nonlinear partial differential equations.

[BibT_eX]

[DOI]

,

George E. Karniadakis

J. Comput. Phys., 2018

Application of local improvements to reduced-order models to sampling methods for nonlinear PDEs with noise.

[BibT_eX]

[DOI]

,

Padmanabhan Seshaiyer

Int. J. Comput. Math., 2018

Deep Learning of Turbulent Scalar Mixing.

[BibT_eX]

[DOI]

,

,

CoRR, 2018

Deep Learning of Vortex Induced Vibrations.

[BibT_eX]

[DOI]

,

,

Michael S. Triantafyllou

,

George E. Karniadakis

CoRR, 2018

Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data.

[BibT_eX]

[DOI]

,

Alireza Yazdani

,

George E. Karniadakis

CoRR, 2018

Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations.

[BibT_eX]

[DOI]

CoRR, 2018

2017

Machine learning of linear differential equations using Gaussian processes.

[BibT_eX]

[DOI]

,

Paris Perdikaris

,

George E. Karniadakis

J. Comput. Phys., 2017

Inferring solutions of differential equations using noisy multi-fidelity data.

[BibT_eX]

[DOI]

,

Paris Perdikaris

,

George E. Karniadakis

J. Comput. Phys., 2017

Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations.

[BibT_eX]

[DOI]

,

Paris Perdikaris

,

George E. Karniadakis

CoRR, 2017

Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

[BibT_eX]

[DOI]

,

Paris Perdikaris

,

George E. Karniadakis

CoRR, 2017

Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations.

[BibT_eX]

[DOI]

,

Paris Perdikaris

,

George E. Karniadakis

CoRR, 2017

Machine Learning of Linear Differential Equations using Gaussian Processes.

[BibT_eX]

[DOI]

,

George E. Karniadakis

CoRR, 2017

Parametric Gaussian Process Regression for Big Data.

[BibT_eX]

[DOI]

CoRR, 2017

2016

Deep Multi-fidelity Gaussian Processes.

[BibT_eX]

[DOI]

,

George E. Karniadakis

CoRR, 2016

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