This article explores how physics-informed neural networks (PINNs) can simulate shock wave generation, interactions, and entropy solutions. Using Burgers’ equation as a test case, the models accurately handle wave formation, collisions, and rarefaction without prior knowledge of origin points. The results highlight how deep learning can advance computational fluid dynamics by tackling problems once limited to traditional numerical methods.This article explores how physics-informed neural networks (PINNs) can simulate shock wave generation, interactions, and entropy solutions. Using Burgers’ equation as a test case, the models accurately handle wave formation, collisions, and rarefaction without prior knowledge of origin points. The results highlight how deep learning can advance computational fluid dynamics by tackling problems once limited to traditional numerical methods.

Shocks, Collisions, and Entropy—Neural Networks Handle It All

2025/09/20 19:00
3 min read
For feedback or concerns regarding this content, please contact us at crypto.news@mexc.com

Abstract and 1. Introduction

1.1. Introductory remarks

1.2. Basics of neural networks

1.3. About the entropy of direct PINN methods

1.4. Organization of the paper

  1. 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

  2. Gradient descent algorithm and efficient implementation

    3.1. Classical gradient descent algorithm for HCLs

    3.2. Gradient descent and domain decomposition methods

  3. 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

  4. Conclusion and References

4.3. Shock wave generation

In this section, we demonstrate the potential of our algorithms to handle shock wave generation, as described in Subsection 2.3. One of the strengths of the proposed algorithm

\

\ is that it does not require to know the initial position&time of birth, in order to accurately track the DLs. Recall that the principle is to assume that in a given (sub)domain and from a smooth function a shock wave will eventually be generated. Hence we decompose the corresponding (sub)domain in two subdomains and consider three neural networks: two neural networks will approximate the solution in each subdomain, and one neural network will approximate the DL. As long as the shock wave is not generated (say for t < t∗ ), the global solution remains smooth and the Rankine-Hugoniot condition is trivially satisfied (null jump); hence the DL for t < t∗ does not have any meaning.

\ Experiment 4. We again consider the inviscid Burgers’ equation, Ω × [0, T] = (−1, 2) × [0, 0.5] and the initial condition

\

\

\ Figure 7: Experiment 4. (Left) Loss function. (Right) Space-time solution

\ Figure 8: Experiment 4. (Left) Graph of the solution at T = 3/5. (Middle) Discontinuity lines. (Right) Flux jump along the DLs.

\

4.4. Shock-Shock interaction

In this subsection, we are proposing a test involving the interaction of two shock waves merging to generate a third shock wave. As explained in Subsection 2.4, in this case it is necessary re-decompose the full domain once the two shock waves have interacted.

\ \

\ \ \ Figure 9: Experiment 5. (Left) Space-time solution without shock interaction (artificial for t > t∗ = 0.45. (Right) Space-time solution with shock interaction.

\

4.5. Entropy solution

We propose here an experiment dedicated to the computation of the viscous shock profiles and rarefaction waves and illustrating the discussion from Subsection 1.3. In this example, a regularized non-entropic shock is shown to be “destabilized” into rarefaction wave by the direct PINN method.

\ \

\ \ \ \

\ \ \

\ \

:::info 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).

:::


:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.

:::

\

Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact crypto.news@mexc.com for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.

You May Also Like

Why the UK Is Seeing an Uplift in Property Sales in 2026

Why the UK Is Seeing an Uplift in Property Sales in 2026

After several turbulent years for the housing market, the UK property sector is showing signs of renewed momentum in 2026. While the market remains cautious, several
Share
Techbullion2026/03/05 01:17
CME Group to launch options on XRP and SOL futures

CME Group to launch options on XRP and SOL futures

The post CME Group to launch options on XRP and SOL futures appeared on BitcoinEthereumNews.com. CME Group will offer options based on the derivative markets on Solana (SOL) and XRP. The new markets will open on October 13, after regulatory approval.  CME Group will expand its crypto products with options on the futures markets of Solana (SOL) and XRP. The futures market will start on October 13, after regulatory review and approval.  The options will allow the trading of MicroSol, XRP, and MicroXRP futures, with expiry dates available every business day, monthly, and quarterly. The new products will be added to the existing BTC and ETH options markets. ‘The launch of these options contracts builds on the significant growth and increasing liquidity we have seen across our suite of Solana and XRP futures,’ said Giovanni Vicioso, CME Group Global Head of Cryptocurrency Products. The options contracts will have two main sizes, tracking the futures contracts. The new market will be suitable for sophisticated institutional traders, as well as active individual traders. The addition of options markets singles out XRP and SOL as liquid enough to offer the potential to bet on a market direction.  The options on futures arrive a few months after the launch of SOL futures. Both SOL and XRP had peak volumes in August, though XRP activity has slowed down in September. XRP and SOL options to tap both institutions and active traders Crypto options are one of the indicators of market attitudes, with XRP and SOL receiving a new way to gauge sentiment. The contracts will be supported by the Cumberland team.  ‘As one of the biggest liquidity providers in the ecosystem, the Cumberland team is excited to support CME Group’s continued expansion of crypto offerings,’ said Roman Makarov, Head of Cumberland Options Trading at DRW. ‘The launch of options on Solana and XRP futures is the latest example of the…
Share
BitcoinEthereumNews2025/09/18 00:56
Shiba Inu Coin Burn Mechanics: How Many SHIB Coins Have Been Burned so Far?

Shiba Inu Coin Burn Mechanics: How Many SHIB Coins Have Been Burned so Far?

Shiba Inu coin burn explained: how SHIB tokens are removed from circulation, why over 410T tokens were burned, and how Shibarium affects supply and price.
Share
coincheckup2026/03/05 00:52