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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...
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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...
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01/30/2023
Temporal Consistency Loss for Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) have been widely used to solve ...
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01/26/2023
Open Problems in Applied Deep Learning
This work formulates the machine learning mechanism as a bi-level optimi...
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10/11/2021
Disease Informed Neural Networks
We introduce Disease Informed Neural Networks (DINNs) – neural networks ...
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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...
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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...
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11/17/2018
Deep Learning of Turbulent Scalar Mixing
Based on recent developments in physics-informed deep learning and deep ...
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08/26/2018
Deep Learning of Vortex Induced Vibrations
Vortex induced vibrations of bluff bodies occur when the vortex shedding...
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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...
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08/02/2018
Machine Learning of Space-Fractional Differential Equations
Data-driven discovery of "hidden physics" -- i.e., machine learning of d...
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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...
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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 ...
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01/04/2018
Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems
The process of transforming observed data into predictive mathematical m...
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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...
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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...
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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...
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04/11/2017
Parametric Gaussian Process Regression for Big Data
This work introduces the concept of parametric Gaussian processes (PGPs)...
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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...
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01/10/2017
Machine Learning of Linear Differential Equations using Gaussian Processes
This work leverages recent advances in probabilistic machine learning to...
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04/26/2016
