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Found 22 papers, 17 papers with code
A Survey on Physics Informed Reinforcement Learning: Review and Open Problems
We present a thorough review of the literature on incorporating physics information, as known as physics priors, in reinforcement learning approaches, commonly referred to as physics-informed reinforcement learning (PIRL).
Real Estate Property Valuation using Self-Supervised Vision Transformers
Our proposed algorithm uses a combination of machine learning, computer vision and hedonic pricing models trained on real estate data to estimate the value of a given property.
Temporal Consistency Loss for Physics-Informed Neural Networks
This paper proposes a method for scaling the mean squared loss terms in the objective function used to train PINNs.
Open Problems in Applied Deep Learning
This work formulates the machine learning mechanism as a bi-level optimization problem.
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing
This study aims to present a review of the four neural network frameworks (i. e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research.
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
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 itself.
Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks
This approach builds on a successful physics informed neural network approaches that have been applied to a variety of applications that can be modeled by linear and non-linear ordinary and partial differential equations.
A deep learning framework for solution and discovery in solid mechanics
We also show the applicability of the framework for transfer learning, and find vastly accelerated convergence during network re-training.
Deep Learning of Vortex Induced Vibrations
Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field.
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 learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navier-Stokes equations.
Machine Learning of Space-Fractional Differential Equations
We extend this framework to linear space-fractional differential equations.
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations
Classical numerical methods for solving partial differential equations suffer from the curse dimensionality mainly due to their reliance on meticulously generated spatio-temporal grids.
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
A long-standing problem at the interface of artificial intelligence and applied mathematics is to devise an algorithm capable of achieving human level or even superhuman proficiency in transforming observed data into predictive mathematical models of the physical world.
Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems
The process of transforming observed data into predictive mathematical models of the physical world has always been paramount in science and engineering.
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations.
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations.
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire.
Parametric Gaussian Process Regression for Big Data
This work introduces the concept of parametric Gaussian processes (PGPs), which is built upon the seemingly self-contradictory idea of making Gaussian processes parametric.
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations
Numerical Gaussian processes, by construction, are designed to deal with cases where: (1) all we observe are noisy data on black-box initial conditions, and (2) we are interested in quantifying the uncertainty associated with such noisy data in our solutions to time-dependent partial differential equations.
Machine Learning of Linear Differential Equations using Gaussian Processes
This work leverages recent advances in probabilistic machine learning to discover conservation laws expressed by parametric linear equations.
Inferring solutions of differential equations using noisy multi-fidelity data
For more than two centuries, solutions of differential equations have been obtained either analytically or numerically based on typically well-behaved forcing and boundary conditions for well-posed problems.
Deep Multi-fidelity Gaussian Processes
We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000).
