Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Abstract and 1. Introduction 1.1. Introductory remarks 1.1. Introductory remarks 1.2. Basics of neural networks 1.2. Basics of neural networks 1.3. About the entropy of direct PINN methods 1.3. About the entropy of direct PINN methods 1.4. Organization of the paper 1.4. Organization of the paper Non-diffusive neural network solver for one dimensional scalar HCLs
2.1. One shock wave
2.2. Arbitrary number of shock waves
2.3. Shock wave generation
2.4. Shock wave interaction
2.5. Non-diffusive neural network solver for one dimensional systems of CLs
2.6. Efficient initial wave decomposition


Gradient descent algorithm and efficient implementation
3.1. Classical gradient descent algorithm for HCLs
3.2. Gradient descent and domain decomposition methods


Numerics
4.1. Practical implementations
4.2. Basic tests and convergence for 1 and 2 shock wave problems
4.3. Shock wave generation
4.4. Shock-Shock interaction
4.5. Entropy solution
4.6. Domain decomposition
4.7. Nonlinear systems


Conclusion and References Non-diffusive neural network solver for one dimensional scalar HCLs
2.1. One shock wave
2.2. Arbitrary number of shock waves
2.3. Shock wave generation
2.4. Shock wave interaction
2.5. Non-diffusive neural network solver for one dimensional systems of CLs
2.6. Efficient initial wave decomposition Non-diffusive neural network solver for one dimensional scalar HCLs Non-diffusive neural network solver for one dimensional scalar HCLs Non-diffusive neural network solver for one dimensional scalar HCLs 2.1. One shock wave 2.1. One shock wave 2.2. Arbitrary number of shock waves 2.2. Arbitrary number of shock waves 2.3. Shock wave generation 2.3. Shock wave generation 2.4. Shock wave interaction 2.4. Shock wave interaction 2.5. Non-diffusive neural network solver for one dimensional systems of CLs 2.5. Non-diffusive neural network solver for one dimensional systems of CLs 2.6. Efficient initial wave decomposition 2.6. Efficient initial wave decomposition Gradient descent algorithm and efficient implementation
3.1. Classical gradient descent algorithm for HCLs
3.2. Gradient descent and domain decomposition methods Gradient descent algorithm and efficient implementation Gradient descent algorithm and efficient implementation 3.1. Classical gradient descent algorithm for HCLs 3.1. Classical gradient descent algorithm for HCLs 3.2. Gradient descent and domain decomposition methods 3.2. Gradient descent and domain decomposition methods Numerics
4.1. Practical implementations
4.2. Basic tests and convergence for 1 and 2 shock wave problems
4.3. Shock wave generation
4.4. Shock-Shock interaction
4.5. Entropy solution
4.6. Domain decomposition
4.7. Nonlinear systems Numerics Numerics 4.1. Practical implementations 4.1. Practical implementations 4.2. Basic tests and convergence for 1 and 2 shock wave problems 4.2. Basic tests and convergence for 1 and 2 shock wave problems 4.3. Shock wave generation 4.3. Shock wave generation 4.4. Shock-Shock interaction 4.4. Shock-Shock interaction 4.5. Entropy solution 4.5. Entropy solution 4.6. Domain decomposition 4.6. Domain decomposition 4.7. Nonlinear systems 4.7. Nonlinear systems Conclusion and References Conclusion and References Conclusion and References Conclusion and References 4.1. Practical implementations This subsection is devoted to the practical aspects of the training process of neural networks. The implementation of the algorithms above is performed using the library neural network jax, see [26]. Although the algorithms look complex, they are actually very easy to implement using jax and we did not face any difficulty in the tuning of the hyper-parameters. In this paper we propose a proof-of-concept of a novel method in low dimension, and which ultimately deals with simple (piecewise-)smooth functions. As a consequence, we have not addressed in details questions related to the choice of the optimization algorithm or of the hyper-parameters, because in this setting they are not particularly relevant. In our numerical simulations we have considered tanh neural networks with one or two hidden layers. The learning nodes to approximate the PDE residuals are randomly selected in the rectangular regions R = (0, 1) × (0, T) (see Subsection 2.1). The weights λ, µ in (12) and (21) are taken equal to 1/2, and more generally for equations with several shock waves or for systems, an equal weight is given to each contribution of the loss functions. Moreover the neural In all the numerical experiments below we consider the problem (1a)-(1b), and in the following experiments we only specify Ω × [0, T], f(u) and u0. We refer to the results with our algorithms as NDNN solution. 4.2. Basic tests and convergence for 1 and 2 shock wave problems In this subsection, we do not consider any domain decomposition, so that only one global loss function is minimized as described in Subsections 2.1, 2.2. Experiment 1. In this experiment we consider Ω × [0, T] = (−4, 1) × [0, 3/4] with f(u) = 4u(2 − u). The initial data is given by Experiment 1 In the time interval [0, 1/2], it is constituted by a rarefaction and a shock wave with constant velocity. Then, in the time interval [1/2, 3/4] the initial shock wave interacts with the rarefaction wave to produce a new shock with non-constant velocity. More specifically the solution is given by Here γ is the DL and it solves for t ∈ [1/2, 1] and γ(1/2) = 0. Let us mention that using the same numerical data, a direct PINN algorithm provides a very inaccurate approximation of the stationary then non-stationary shock waves, while our algorithm provides accurate approximations. This last point is discussed in the 2 following tests. These experiments allow to validate the convergence of the proposed approach. Authors:
(1) Emmanuel LORIN, School of Mathematics and Statistics, Carleton University, Ottawa, Canada, K1S 5B6 and Centre de Recherches Mathematiques, Universit´e de Montr´eal, Montreal, Canada, H3T 1J4 (elorin@math.carleton.ca);
(2) Arian NOVRUZI, a Corresponding Author from Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON K1N 6N5, Canada (novruzi@uottawa.ca). Authors: Authors: (1) Emmanuel LORIN, School of Mathematics and Statistics, Carleton University, Ottawa, Canada, K1S 5B6 and Centre de Recherches Mathematiques, Universit´e de Montr´eal, Montreal, Canada, H3T 1J4 (elorin@math.carleton.ca); (2) Arian NOVRUZI, a Corresponding Author from Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON K1N 6N5, Canada (novruzi@uottawa.ca). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

How Scientists Taught AI to Handle Shock Waves

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A Comparative Performance Analysis of SymTax on Five Citation Recommendation Datasets

A Radical Neural Network Approach to Modeling Shock Dynamics

Can Neural Networks Capture Shock Waves Without Diffusion? This Paper Says Yes

Neural Networks vs. Scalar Shock Waves

Neural Network Tools for Shock Wave Decomposition

Why Gradient Descent Converges (and Sometimes Doesn’t) in Neural Networks

A Comparative Performance Analysis of SymTax on Five Citation Recommendation Datasets

A Radical Neural Network Approach to Modeling Shock Dynamics

Can Neural Networks Capture Shock Waves Without Diffusion? This Paper Says Yes

Neural Networks vs. Scalar Shock Waves

Neural Network Tools for Shock Wave Decomposition

Why Gradient Descent Converges (and Sometimes Doesn’t) in Neural Networks

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps