1.4k citations · 5.1k across the 32 of their papers we have counts for
13 papers · 1 filter
Physics-Informed Neural Networks and Extensions
Maziar Raissi, Paris Perdikaris, Nazanin Ahmadi +1
In this paper, we review the new method Physics-Informed Neural Networks (PINNs) that has become the main pillar in scientific machine learning, we present recent practical extensi…
Physics-Informed Machine Learning for Smart Additive Manufacturing
Rahul Sharma, Maziar Raissi, Y. B. Guo
Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data…
A Survey on Physics Informed Reinforcement Learning: Review and Open Problems
Chayan Banerjee, Kien Nguyen, Clinton Fookes +1
The inclusion of physical information in machine learning frameworks has revolutionized many application areas. This involves enhancing the learning process by incorporating physic…
Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics
R. Sharma, W. Grace Guo, M. Raissi +1
Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based sim…
Open Problems in Applied Deep Learning
Maziar Raissi
This work formulates the machine learning mechanism as a bi-level optimization problem. The inner level optimization loop entails minimizing a properly chosen loss function evaluat…
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing
Salah A Faroughi, Nikhil Pawar, Celio Fernandes +4
Recent breakthroughs in computing power have made it feasible to use machine learning and deep learning to advance scientific computing in many fields, including fluid mechanics, s…
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
Salvatore Cuomo, Vincenzo Schiano di Cola, Fabio Giampaolo +3
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itsel…
Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks
Sagi Shaier, Maziar Raissi, Padmanabhan Seshaiyer
In this work, we present an approach called Disease Informed Neural Networks (DINNs) that can be employed to effectively predict the spread of infectious diseases. This approach bu…
A deep learning framework for solution and discovery in solid mechanics
Ehsan Haghighat, Maziar Raissi, Adrian Moure +2
We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporat…
Machine Learning of Space-Fractional Differential Equations
Mamikon Gulian, Maziar Raissi, Paris Perdikaris +1
Data-driven discovery of "hidden physics" -- i.e., machine learning of differential equation models underlying observed data -- has recently been approached by embedding the discov…
Machine Learning of Linear Differential Equations using Gaussian Processes
Maziar Raissi, George Em. Karniadakis
This work leverages recent advances in probabilistic machine learning to discover conservation laws expressed by parametric linear equations. Such equations involve, but are not li…
Inferring solutions of differential equations using noisy multi-fidelity data
Maziar Raissi, Paris Perdikaris, George Em. Karniadakis
For more than two centuries, solutions of differential equations have been obtained either analytically or numerically based on typically well-behaved forcing and boundary conditio…
Deep Multi-fidelity Gaussian Processes
Maziar Raissi, George Karniadakis
We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000). Our method can handle general discontinuous cr…