
Disease Informed Neural Networks
We introduce Disease Informed Neural Networks (DINNs) – neural networks ...
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A deep learning framework for solution and discovery in solid mechanics
We present the application of a class of deep learning, known as Physics...
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A deep learning framework for solution and discovery in solid mechanics: linear elasticity
We present the application of a class of deep learning, known as Physics...
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Deep Learning of Turbulent Scalar Mixing
Based on recent developments in physicsinformed deep learning and deep ...
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Deep Learning of Vortex Induced Vibrations
Vortex induced vibrations of bluff bodies occur when the vortex shedding...
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Hidden Fluid Mechanics: A NavierStokes Informed Deep Learning Framework for Assimilating Flow Visualization Data
We present hidden fluid mechanics (HFM), a physics informed deep learnin...
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Machine Learning of SpaceFractional Differential Equations
Datadriven discovery of "hidden physics"  i.e., machine learning of d...
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ForwardBackward Stochastic Neural Networks: Deep Learning of Highdimensional Partial Differential Equations
Classical numerical methods for solving partial differential equations s...
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Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
A longstanding problem at the interface of artificial intelligence and ...
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Multistep Neural Networks for Datadriven Discovery of Nonlinear Dynamical Systems
The process of transforming observed data into predictive mathematical m...
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Physics Informed Deep Learning (Part II): Datadriven Discovery of Nonlinear Partial Differential Equations
We introduce physics informed neural networks  neural networks that ar...
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Physics Informed Deep Learning (Part I): Datadriven Solutions of Nonlinear Partial Differential Equations
We introduce physics informed neural networks  neural networks that ar...
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Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
While there is currently a lot of enthusiasm about "big data", useful da...
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Parametric Gaussian Process Regression for Big Data
This work introduces the concept of parametric Gaussian processes (PGPs)...
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Numerical Gaussian Processes for Timedependent and Nonlinear Partial Differential Equations
We introduce the concept of numerical Gaussian processes, which we defin...
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Machine Learning of Linear Differential Equations using Gaussian Processes
This work leverages recent advances in probabilistic machine learning to...
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Deep Multifidelity Gaussian Processes
We develop a novel multifidelity framework that goes far beyond the cla...
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Maziar Raissi
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