Maziar Raissi

According to our database, Maziar Raissi authored at least 51 papers between 2016 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries.

Reza Akbarian Bafghi, Sukirt Thakur, Maziar Raissi

CoRR, May, 2026

Online Optimization with Unknown Time-Varying Parameters from Noisy Gradient Measurements.

Shivanshu Tripathi, Maziar Raissi

CoRR, May, 2026

Understanding Tool-Augmented Agents for Lean Formalization: A Factorial Analysis.

Ke Zhang, Patricio Gallardo, Maziar Raissi, Sudhir Murthy

CoRR, April, 2026

Learning Parameterized Nonlinear Elasticity on Curved Surfaces.

Yankang Liu, Ke Zhang, Maziar Raissi, Roya Zandi

CoRR, April, 2026

Meta-Learning-Based Surrogate Models for Efficient Hyperparameter Optimization.

Liping Deng, Maziar Raissi, Mingqing Xiao

IEEE Trans. Pattern Anal. Mach. Intell., March, 2026

Test-Driven Agentic Framework for Reliable Robot Controller.

Shivanshu Tripathi, Reza Akbarian Bafghi, Maziar Raissi

CoRR, March, 2026

Deep Neural Network Parameter Selection via Dataset Similarity Under Meta-Learning Framework.

Liping Deng, Maziar Raissi, Mingqing Xiao

IEEE Trans. Pattern Anal. Mach. Intell., February, 2026

NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations.

Maedeh Makki, Satish Chandran, Maziar Raissi, Adrien Grenier, Behzad Mohebbi

CoRR, January, 2026

PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics.

Sukirt Thakur, Marcus Roper, Yang Zhou, Reza Akbarian Bafghi, Brahmajee K. Nallamothu, C. Alberto Figueroa, Srinivas Paruchuri, Scott Burger, Maziar Raissi

CoRR, January, 2026

Data-Efficient Physics-Informed Learning to Model Synchro-Waveform Dynamics of Grid-Integrated Inverter-Based Resources.

Shivanshu Tripathi, Hossein Mohsenzadeh-Yazdi, Maziar Raissi, Hamed Mohsenian Rad

CoRR, January, 2026

Exploring Multiple Timescale Dynamics Using Geometric Singular Perturbation-Informed Neural Networks (GSPINNs).

Hamid Mofidi, Maziar Raissi, Wuyue Yang

SIAM J. Sci. Comput., 2026

2025

Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation.

Reza Akbarian Bafghi, Carden Bagwell, Avinash Ravichandran, Ashish Shrivastava, Maziar Raissi

CoRR, January, 2025

A survey on physics informed reinforcement learning: Review and open problems.

Chayan Banerjee, Kien Nguyen Thanh, Clinton Fookes, Maziar Raissi

Expert Syst. Appl., 2025

MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representations.

Reza Akbarian Bafghi, Nidhin Harilal, Claire Monteleoni, Maziar Raissi

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2025

Aligning to What? Limits to RLHF Based Alignment.

Logan Barnhart, Reza Akbarian Bafghi, Stephen Becker, Maziar Raissi

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2025, Albuquerque, New Mexico, USA, April 29, 2025

2024

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics.

Salah A. Faroughi, Nikhil M. Pawar, Célio Fernandes, Maziar Raissi, Subasish Das, Nima K. Kalantari, Seyed Kourosh Mahjour

J. Comput. Inf. Sci. Eng., April, 2024

Guest Editorial: Special Issue on Physics-Informed Machine Learning.

Francesco Piccialli, Maziar Raissi, Felipe A. C. Viana, Giancarlo Fortino, Huimin Lu, Amir Hussain

IEEE Trans. Artif. Intell., March, 2024

Where Did Your Model Learn That? Label-free Influence for Self-supervised Learning.

Nidhin Harilal, Amit Kiran Rege, Reza Akbarian Bafghi, Maziar Raissi, Claire Monteleoni

CoRR, 2024

Physics-Informed Neural Networks and Extensions.

Maziar Raissi, Paris Perdikaris, Nazanin Ahmadi Daryakenari, George Em Karniadakis

CoRR, 2024

Physics-Informed Machine Learning for Smart Additive Manufacturing.

Rahul Sharma, Maziar Raissi, Y. B. Guo

CoRR, 2024

Mixing Natural and Synthetic Images for Robust Self-Supervised Representations.

Reza Akbarian Bafghi, Nidhin Harilal, Claire Monteleoni, Maziar Raissi

CoRR, 2024

Deep LPPLS: Forecasting of temporal critical points in natural, engineering and financial systems.

Joshua Nielsen, Didier Sornette, Maziar Raissi

CoRR, 2024

Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting.

Reza Akbarian Bafghi, Nidhin Harilal, Claire Monteleoni, Maziar Raissi

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

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

Reza Akbarian Bafghi, Maziar Raissi

CoRR, 2023

Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics.

Rahul Sharma, W. Grace Guo, Maziar Raissi, Yuebin Guo

CoRR, 2023

Real Estate Property Valuation using Self-Supervised Vision Transformers.

Mahdieh Yazdani, Maziar Raissi

CoRR, 2023

Temporal Consistency Loss for Physics-Informed Neural Networks.

Sukirt Thakur, Maziar Raissi, Harsa Mitra, Arezoo M. Ardekani

CoRR, 2023

Open Problems in Applied Deep Learning.

Maziar Raissi

CoRR, 2023

2022

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

Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo, Gianluigi Rozza, Maziar Raissi, Francesco Piccialli

J. Sci. Comput., 2022

2021

Disease Informed Neural Networks.

Sagi Shaier, Maziar Raissi

CoRR, 2021

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

Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Stephen Becker, Maziar Raissi

Big Data, 2021

2020

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

Alireza Yazdani, Lu Lu, Maziar Raissi, George Em Karniadakis

PLoS Comput. Biol., 2020

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

Ehsan Haghighat, Maziar Raissi, Adrian Moure, Héctor Gómez, Ruben Juanes

CoRR, 2020

2019

Machine Learning of Space-Fractional Differential Equations.

Mamikon A. Gulian, Maziar Raissi, 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.

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

J. Comput. Phys., 2019

2018

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

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

SIAM J. Sci. Comput., 2018

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

Maziar Raissi

J. Mach. Learn. Res., 2018

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

Maziar Raissi, George E. Karniadakis

J. Comput. Phys., 2018

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

Maziar Raissi, Padmanabhan Seshaiyer

Int. J. Comput. Math., 2018

Deep Learning of Turbulent Scalar Mixing.

Maziar Raissi, Hessam Babaee, Peyman Givi

CoRR, 2018

Deep Learning of Vortex Induced Vibrations.

Maziar Raissi, Zhicheng Wang, Michael S. Triantafyllou, George E. Karniadakis

CoRR, 2018

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

Maziar Raissi, Alireza Yazdani, George E. Karniadakis

CoRR, 2018

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

Maziar Raissi

CoRR, 2018

2017

Machine learning of linear differential equations using Gaussian processes.

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

J. Comput. Phys., 2017

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

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

J. Comput. Phys., 2017

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

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

CoRR, 2017

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

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

CoRR, 2017

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

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

CoRR, 2017

Machine Learning of Linear Differential Equations using Gaussian Processes.

Maziar Raissi, George E. Karniadakis

CoRR, 2017

Parametric Gaussian Process Regression for Big Data.

Maziar Raissi

CoRR, 2017

2016

Deep Multi-fidelity Gaussian Processes.

Maziar Raissi, George E. Karniadakis

CoRR, 2016