Maziar Raissi | DeepAI

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∙ 09/05/2023

A Survey on Physics Informed Reinforcement Learning: Review and Open Problems

The inclusion of physical information in machine learning frameworks has...

0 Chayan Banerjee, et al. ∙

∙ 01/31/2023

Real Estate Property Valuation using Self-Supervised Vision Transformers

The use of Artificial Intelligence (AI) in the real estate market has be...

0 Mahdieh Yazdani, et al. ∙

∙ 01/30/2023

Temporal Consistency Loss for Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) have been widely used to solve ...

0 Sukirt Thakur, et al. ∙

∙ 01/26/2023

Open Problems in Applied Deep Learning

This work formulates the machine learning mechanism as a bi-level optimi...

0 Maziar Raissi, et al. ∙

∙ 10/11/2021

Disease Informed Neural Networks

We introduce Disease Informed Neural Networks (DINNs) – neural networks ...

0 Sagi Shaier, et al. ∙

∙ 02/14/2020

A deep learning framework for solution and discovery in solid mechanics

We present the application of a class of deep learning, known as Physics...

1 Ehsan Haghighat, et al. ∙

∙ 02/14/2020

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...

1 Ehsan Haghighat, et al. ∙

∙ 11/17/2018

Deep Learning of Turbulent Scalar Mixing

Based on recent developments in physics-informed deep learning and deep ...

0 Maziar Raissi, et al. ∙

∙ 08/26/2018

Deep Learning of Vortex Induced Vibrations

Vortex induced vibrations of bluff bodies occur when the vortex shedding...

0 Maziar Raissi, et al. ∙

∙ 08/13/2018

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

We present hidden fluid mechanics (HFM), a physics informed deep learnin...

2 Maziar Raissi, et al. ∙

∙ 08/02/2018

Machine Learning of Space-Fractional Differential Equations

Data-driven discovery of "hidden physics" -- i.e., machine learning of d...

0 Mamikon Gulian, et al. ∙

∙ 04/19/2018

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

Classical numerical methods for solving partial differential equations s...

0 Maziar Raissi, et al. ∙

∙ 01/20/2018

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

A long-standing problem at the interface of artificial intelligence and ...

0 Maziar Raissi, et al. ∙

∙ 01/04/2018

Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems

The process of transforming observed data into predictive mathematical m...

0 Maziar Raissi, et al. ∙

∙ 11/28/2017

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

We introduce physics informed neural networks -- neural networks that ar...

0 Maziar Raissi, et al. ∙

∙ 11/28/2017

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

We introduce physics informed neural networks -- neural networks that ar...

0 Maziar Raissi, et al. ∙

∙ 08/02/2017

Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations

While there is currently a lot of enthusiasm about "big data", useful da...

0 Maziar Raissi, et al. ∙

∙ 04/11/2017

Parametric Gaussian Process Regression for Big Data

This work introduces the concept of parametric Gaussian processes (PGPs)...

0 Maziar Raissi, et al. ∙

∙ 03/29/2017

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

We introduce the concept of numerical Gaussian processes, which we defin...

0 Maziar Raissi, et al. ∙

∙ 01/10/2017

Machine Learning of Linear Differential Equations using Gaussian Processes

This work leverages recent advances in probabilistic machine learning to...

0 Maziar Raissi, et al. ∙

∙ 04/26/2016

Deep Multi-fidelity Gaussian Processes

We develop a novel multi-fidelity framework that goes far beyond the cla...

0 Maziar Raissi, et al. ∙

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