• Corpus ID: 203610380

# Blending Diverse Physical Priors with Neural Networks

@article{Ba2019BlendingDP,
title={Blending Diverse Physical Priors with Neural Networks},
author={Yunhao Ba and Guangyuan Zhao and Achuta Kadambi},
journal={ArXiv},
year={2019},
volume={abs/1910.00201}
}
• Published 25 September 2019
• Computer Science, Physics
• ArXiv
Machine learning in context of physical systems merits a re-examination of the learning strategy. In addition to data, one can leverage a vast library of physical prior models (e.g. kinematics, fluid flow, etc) to perform more robust inference. The nascent sub-field of \emph{physics-based learning} (PBL) studies the blending of neural networks with physical priors. While previous PBL algorithms have been applied successfully to specific tasks, it is hard to generalize existing PBL methods to a…
18 Citations

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## References

SHOWING 1-10 OF 53 REFERENCES

### Physics-based Neural Networks for Shape from Polarization

• Physics
ArXiv
• 2019
This work study the blending of physics and deep learning in the context of Shape from Polarization (SfP) in the framework of a two-stage encoder, finding that there is a subtlety to combining physics and neural networks.

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

• Computer Science
ArXiv
• 2017
This two part treatise introduces 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 and demonstrates how these networks can be used to infer solutions topartial differential equations, and obtain physics-informed surrogate models that are fully differentiable with respect to all input coordinates and free parameters.

### End-to-End Differentiable Physics for Learning and Control

• Computer Science
NeurIPS
• 2018
This paper demonstrates how to perform backpropagation analytically through a physical simulator defined via a linear complementarity problem, and highlights the system's ability to learn physical parameters from data, efficiently match and simulate observed visual behavior, and readily enable control via gradient-based planning methods.

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

• M. Raissi
• Computer Science
J. Mach. Learn. Res.
• 2018
This work puts forth a deep learning approach for discovering nonlinear partial differential equations from scattered and potentially noisy observations in space and time by approximate the unknown solution as well as the nonlinear dynamics by two deep neural networks.

### Unrolled Optimization with Deep Priors

• Computer Science, Mathematics
ArXiv
• 2017
This paper presents unrolled optimization with deep priors, a principled framework for infusing knowledge of the image formation into deep networks that solve inverse problems in imaging, inspired by classical iterative methods.

### PDE-Net: Learning PDEs from Data

• Computer Science
ICML
• 2018
Numerical experiments show that the PDE-Net has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.

### Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling

• Computer Science
ArXiv
• 2017
A novel framework, termed as physics-guided neural network (PGNN), leverages the output of physics-based model simulations along with observational features to generate predictions using a neural network architecture to ensure better generalizability as well as physical consistency of results.

### Label-Free Supervision of Neural Networks with Physics and Domain Knowledge

• Computer Science
AAAI
• 2017
This work introduces a new approach to supervising neural networks by specifying constraints that should hold over the output space, rather than direct examples of input-output pairs, derived from prior domain knowledge.

### Neural Architecture Search with Reinforcement Learning

• Computer Science
ICLR
• 2017
This paper uses a recurrent network to generate the model descriptions of neural networks and trains this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set.

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

• Computer Science
ArXiv
• 2017
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