Deep Multi-fidelity Gaussian Processes. - Maziar Raissi; George E. Karniadakis (CoRR, 2016)

Parametric Gaussian Process Regression for Big Data. - Maziar Raissi (CoRR, 2017)

Machine Learning of Linear Differential Equations using Gaussian Processes. - Maziar Raissi; 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)

Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations. - Maziar Raissi; Paris Perdikaris; George E. Karniadakis (CoRR, 2017)

Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations. - Maziar Raissi; Paris Perdikaris; George E. Karniadakis (CoRR, 2017)

Inferring solutions of differential equations using noisy multi-fidelity data. - Maziar Raissi; Paris Perdikaris; George E. Karniadakis (J. Comput. Phys., 2017)

Machine learning of linear differential equations using Gaussian processes. - Maziar Raissi; Paris Perdikaris; George E. Karniadakis (J. Comput. Phys., 2017)

Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations. - Maziar Raissi (CoRR, 2018)

Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data. - Maziar Raissi; Alireza Yazdani 001; George E. Karniadakis (CoRR, 2018)

Deep Learning of Vortex Induced Vibrations. - Maziar Raissi; Zhicheng Wang; Michael S. Triantafyllou; George E. Karniadakis (CoRR, 2018)

Deep Learning of Turbulent Scalar Mixing. - Maziar Raissi; Hessam Babaee; Peyman Givi (CoRR, 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)

Hidden physics models: Machine learning of nonlinear partial differential equations. - Maziar Raissi; George E. Karniadakis (J. Comput. Phys., 2018)

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations. - Maziar Raissi (J. Mach. Learn. Res., 2018)

Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations. - Maziar Raissi; Paris Perdikaris; George E. Karniadakis (SIAM J. Sci. Comput., 2018)

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)

Machine Learning of Space-Fractional Differential Equations. - Mamikon A. Gulian; Maziar Raissi; Paris Perdikaris; George E. Karniadakis (SIAM J. Sci. Comput., 2019)

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)

Systems biology informed deep learning for inferring parameters and hidden dynamics. - Alireza Yazdani 001; Lu Lu 010; Maziar Raissi; George Em Karniadakis (PLoS Comput. Biol., 2020)

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)

Disease Informed Neural Networks. - Sagi Shaier; Maziar Raissi (CoRR, 2021)

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)

Open Problems in Applied Deep Learning. - Maziar Raissi (CoRR, 2023)

Temporal Consistency Loss for Physics-Informed Neural Networks. - Sukirt Thakur; Maziar Raissi; Harsa Mitra; Arezoo M. Ardekani (CoRR, 2023)

Real Estate Property Valuation using Self-Supervised Vision Transformers. - Mahdieh Yazdani; 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)

PINNs-TF2: Fast and User-Friendly Physics-Informed Neural Networks in TensorFlow V2. - Reza Akbarian Bafghi; Maziar Raissi (CoRR, 2023)

Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting. - Reza Akbarian Bafghi; Nidhin Harilal; Claire Monteleoni; Maziar Raissi (CVPR Workshops, 2024)

Deep LPPLS: Forecasting of temporal critical points in natural, engineering and financial systems. - Joshua Nielsen; Didier Sornette; Maziar Raissi (CoRR, 2024)

Mixing Natural and Synthetic Images for Robust Self-Supervised Representations. - Reza Akbarian Bafghi; Nidhin Harilal; Claire Monteleoni; Maziar Raissi (CoRR, 2024)

Physics-Informed Machine Learning for Smart Additive Manufacturing. - Rahul Sharma; Maziar Raissi; Y. B. Guo (CoRR, 2024)

Physics-Informed Neural Networks and Extensions. - Maziar Raissi; Paris Perdikaris; Nazanin Ahmadi Daryakenari; George Em Karniadakis (CoRR, 2024)

Where Did Your Model Learn That? Label-free Influence for Self-supervised Learning. - Nidhin Harilal; Amit Rege; Reza Akbarian Bafghi; Maziar Raissi; Claire Monteleoni (CoRR, 2024)

Guest Editorial: Special Issue on Physics-Informed Machine Learning. - Francesco Piccialli; Maziar Raissi; Felipe A. C. Viana; Giancarlo Fortino; Huimin Lu 001; Amir Hussain 001 (IEEE Trans. Artif. Intell., 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., 2024)

Aligning to What? Limits to RLHF Based Alignment. - Logan Barnhart; Reza Akbarian Bafghi; Stephen Becker; Maziar Raissi (NAACL (Findings), 2025)

MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representations. - Reza Akbarian Bafghi; Nidhin Harilal; Claire Monteleoni; Maziar Raissi (WACV, 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)

Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation. - Reza Akbarian Bafghi; Carden Bagwell; Avinash Ravichandran; Ashish Shrivastava; Maziar Raissi (CoRR, 2025)

Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Laser Powder Bed Fusion. - Rahul Sharma; Maziar Raissi; Y. B. Guo (CoRR, 2025)