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
3분 읽기
이 콘텐츠에 대한 의견이나 우려 사항이 있으시면 [email protected]으로 연락주시기 바랍니다

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 ([email protected]);

(2) Arian NOVRUZI, a Corresponding Author from Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON K1N 6N5, Canada ([email protected]).

:::


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

:::

\

시장 기회
인스타댑 로고
인스타댑 가격(FLUID)
$1.3585
$1.3585$1.3585
-0.07%
USD
인스타댑 (FLUID) 실시간 가격 차트

SPACEX(PRE) Launchpad

SPACEX(PRE) LaunchpadSPACEX(PRE) Launchpad

Register for a chance to win a free lucky draw

면책 조항: 본 사이트에 재게시된 글들은 공개 플랫폼에서 가져온 것으로 정보 제공 목적으로만 제공됩니다. 이는 반드시 MEXC의 견해를 반영하는 것은 아닙니다. 모든 권리는 원저자에게 있습니다. 제3자의 권리를 침해하는 콘텐츠가 있다고 판단될 경우, [email protected]으로 연락하여 삭제 요청을 해주시기 바랍니다. MEXC는 콘텐츠의 정확성, 완전성 또는 시의적절성에 대해 어떠한 보증도 하지 않으며, 제공된 정보에 기반하여 취해진 어떠한 조치에 대해서도 책임을 지지 않습니다. 본 콘텐츠는 금융, 법률 또는 기타 전문적인 조언을 구성하지 않으며, MEXC의 추천이나 보증으로 간주되어서는 안 됩니다.

추천 콘텐츠

Non-Opioid Painkillers Have Struggled–Cannabis Drugs Might Be The Solution

Non-Opioid Painkillers Have Struggled–Cannabis Drugs Might Be The Solution

The post Non-Opioid Painkillers Have Struggled–Cannabis Drugs Might Be The Solution appeared on BitcoinEthereumNews.com. In this week’s edition of InnovationRx, we look at possible pain treatments from cannabis, risks of new vaccine restrictions, virtual clinical trials at the Mayo Clinic, GSK’s $30 billion U.S. manufacturing commitment, and more. To get it in your inbox, subscribe here. Despite their addictive nature, opioids continue to be a major treatment for pain due to a lack of effective alternatives. In an effort to boost new drugs, the FDA released new guidelines for non-opioid painkillers last week. But making these drugs hasn’t been easy. Vertex Pharmaceuticals received FDA approval for its non-opioid Journavx in January, then abandoned a next generation drug after a failed clinical trial earlier this summer. Acadia similarly abandoned a promising candidate after a failed trial in 2022. One possible basis for non-opioids might be cannabis. Earlier this year, researchers at Washington University at St. Louis and Stanford published a study showing that a cannabis-derived compound successfully eased pain in mice with minimal side effects. Munich-based pharmaceutical company Vertanical is perhaps the furthest along in this quest. It is developing a cannabinoid-based extract to treat chronic pain it hopes will soon become an approved medicine, first in the European Union and eventually in the United States. The drug, currently called Ver-01, packs enough low levels of cannabinoids (including THC) to relieve pain, but not so much that patients get high. Founder Clemens Fischer, a 50-year-old medical doctor and serial pharmaceutical and supplement entrepreneur, hopes it will become the first cannabis-based painkiller prescribed by physicians and covered by insurance. Fischer founded Vertanical, with his business partner Madlena Hohlefelder, in 2017, and has invested more than $250 million of his own money in it. With a cannabis cultivation site and drug manufacturing plant in Denmark, Vertanical has successfully passed phase III clinical trials in Germany and expects…
공유하기
BitcoinEthereumNews2025/09/18 05:26
Bitcoin shorts risk $2.5 billion liquidation at $72K: Are bears in danger?

Bitcoin shorts risk $2.5 billion liquidation at $72K: Are bears in danger?

Bitcoin is poised for a reversal if ETF demand returns or a ceasefire occurs, potentially crushing short sellers in a massive price squeeze.
공유하기
Coin Telegraph2026/04/04 19:58
Robotics Automation Prototyping: Engineering Kinetic Agility into End-Effectors

Robotics Automation Prototyping: Engineering Kinetic Agility into End-Effectors

Inertia is the invisible tax on modern industrial throughput. Every millisecond a robotic arm spends decelerating, or waiting for high-frequency vibrations to settle
공유하기
Techbullion2026/04/02 18:25

RealStocks Now Live

RealStocks Now LiveRealStocks Now Live

Trade real U.S. stock via regulated brokerage