1.4k citations · 5.1k across the 32 of their papers we have counts for
37 papers
CARS: a centerline-anchored attention surrogate for coronary blood flow on skeleton-generated lumen geometries
Reza Akbarian Bafghi, Sukirt Thakur, Maziar Raissi
When the parameter of a PDE surrogate is the flow domain itself, the key/value set of an attention model must encode that domain. We present CARS (Coronary Artery Rapid Surrogate),…
Are Learned Vascular Tree Generators Better Than Conditional Resampling?
Reza Akbarian Bafghi, Sukirt Thakur, Maziar Raissi
Fidelity scores compare generated vascular trees with held-out real trees, so by themselves they cannot detect reuse of training data. A training-free conditional resampler shows t…
Online Optimization with Unknown Time-Varying Parameters Using Noisy Gradient Measurements
Shivanshu Tripathi, Fabio Pasqualetti, Maziar Raissi
We study online optimization problems in which the cost function depends on latent, time-varying parameters that are unmeasurable and governed by unknown dynamics. Specifically, we…
Learning Parameterized Nonlinear Elasticity on Curved Surfaces
Yankang Liu, Ke Zhang, Maziar Raissi +1
We learn parameterized nonlinear elasticity on curved surfaces using a physics-informed neural network that enforces governing equations and boundary conditions directly through th…
ELPINN: Eulerian Lagrangian Physics-Informed Neural Network
Sukirt Thakur, Maziar Raissi
Physics-Informed Neural Networks (PINNs) have gained widespread popularity for solving inverse and forward problems across a range of scientific and engineering domains. However, m…
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…
MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representations
Reza Akbarian Bafghi, Nidhin Harilal, Claire Monteleoni +1
This paper introduces MixDiff, a new self-supervised learning (SSL) pre-training framework that combines real and synthetic images. Unlike traditional SSL methods that predominantl…
Deep LPPLS: Forecasting of temporal critical points in natural, engineering and financial systems
Joshua Nielsen, Didier Sornette, Maziar Raissi
The Log-Periodic Power Law Singularity (LPPLS) model offers a general framework for capturing dynamics and predicting transition points in diverse natural and social systems. In th…
PINNs-TF2: Fast and User-Friendly Physics-Informed Neural Networks in TensorFlow V2
Reza Akbarian Bafghi, Maziar Raissi
Physics-informed neural networks (PINNs) have gained prominence for their capability to tackle supervised learning tasks that conform to physical laws, notably nonlinear partial di…
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…
Real Estate Property Valuation using Self-Supervised Vision Transformers
Mahdieh Yazdani, Maziar Raissi
The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self…
Temporal Consistency Loss for Physics-Informed Neural Networks
Sukirt Thakur, Maziar Raissi, Harsa Mitra +1
Physics-informed neural networks (PINNs) have been widely used to solve partial differential equations in a forward and inverse manner using deep neural networks. However, training…
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…
ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection
Sukirt Thakur, Maziar Raissi, Arezoo M. Ardekani
Viscoelastic fluids are a class of fluids that exhibit both viscous and elastic nature. Modelling such fluids requires constitutive equations for the stress, and choosing the most…
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…
Deep Learning of Turbulent Scalar Mixing
Maziar Raissi, Hessam Babaee, Peyman Givi
Based on recent developments in physics-informed deep learning and deep hidden physics models, we put forth a framework for discovering turbulence models from scattered and potenti…
Deep Learning of Vortex Induced Vibrations
Maziar Raissi, Zhicheng Wang, Michael S. Triantafyllou +1
Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and…
Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data
Maziar Raissi, Alireza Yazdani, George Em Karniadakis
We present hidden fluid mechanics (HFM), a physics informed deep learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navi…
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…