Maziar Raissi
Orcid: 0000-0002-8467-4568
According to our database1,
Maziar Raissi
authored at least 36 papers
between 2016 and 2024.
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Bibliography
2024
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics.
J. Comput. Inf. Sci. Eng., April, 2024
IEEE Trans. Artif. Intell., March, 2024
CoRR, 2024
CoRR, 2024
Deep LPPLS: Forecasting of temporal critical points in natural, engineering and financial systems.
CoRR, 2024
Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
2023
CoRR, 2023
CoRR, 2023
2022
Scientific Machine Learning Through Physics-Informed Neural Networks: Where we are and What's Next.
J. Sci. Comput., 2022
2021
Call for Special Issue Papers: Big Scientific Data and Machine Learning in Science and Engineering: Deadline for Manuscript Submission: February 1, 2022.
Big Data, 2021
2020
PLoS Comput. Biol., 2020
A deep learning framework for solution and discovery in solid mechanics: linear elasticity.
CoRR, 2020
2019
SIAM J. Sci. Comput., 2019
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.
J. Comput. Phys., 2019
2018
Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations.
SIAM J. Sci. Comput., 2018
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations.
J. Mach. Learn. Res., 2018
J. Comput. Phys., 2018
Application of local improvements to reduced-order models to sampling methods for nonlinear PDEs with noise.
Int. J. Comput. Math., 2018
Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data.
CoRR, 2018
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations.
CoRR, 2018
2017
J. Comput. Phys., 2017
J. Comput. Phys., 2017
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations.
CoRR, 2017
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.
CoRR, 2017
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations.
CoRR, 2017
CoRR, 2017
2016