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Symmetry
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Symmetry in Gravitational Wave Physics
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Symmetry in Gravitational Wave Physics

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Physics".

Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 1140

Special Issue Editor

Dr. Zhi Wang

E-Mail Website
Guest Editor
Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
Interests: space-based gravitational wave detection; payload of GW detection in space, such as: telescope, interferometer, inertia sensor

Special Issue Information

Dear Colleague,

Chinese scientists have proposed two schemes to detect gravitational waves in space, Taiji Program of the Chinese Academy of Sciences and TIANQIN of Sun Yat-sen University that orbit the Sun and the Earth, respectively, and all use triangular formation, but with different arm lengths.

In the “Taiji Program in Space” of the Chinese Academy of Sciences, three satellites in the formation of an equilateral triangle constitute a space-based wave observatory, which runs in the orbit around the Sun. The centre of mass of three satellites falls on the Earth’s orbit and the satellite spacing is 3 million kilometres. Each satellite contains two test masses. For free floating motion of the test masses, the satellites will use the drag-free control technology to protect the test masses against non-conservative force disturbances.

The laser interferometer and Inertial sensor are the key payloads for gravitational wave detection in space, and the accurate measurement of distance change is extremely demanding, the displacement noise budget of the interferometry system is in the order of 8 pm/Hz1/2 (0.1 mHz–1 Hz), and the residual acceleration noise of the inertial sensor is 3 × 10-15 ms-2/Hz1/2 (0.1 mHz–1 Hz) along the measuring axis.

Dr. Zhi Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • gravitational wave
  • laser interferometer
  • telescope
  • inertial sensor

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Published Papers (1 paper)

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Research

17 pages, 4722 KB  
Article
Machine Learning-Driven Conservative-to-Primitive Conversion in Hybrid Piecewise Polytropic and Tabulated Equations of State
by Semih Kacmaz, Roland Haas and E. A. Huerta
Symmetry 2025, 17(9), 1409; https://doi.org/10.3390/sym17091409 - 29 Aug 2025
Cited by 1 | Viewed by 753
Abstract
We present a novel machine learning (ML)-based method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditional root-finding techniques are computationally expensive, particularly for large-scale relativistic hydrodynamics simulations. To address this, we employ feedforward neural networks (NNC2PS [...] Read more.
We present a novel machine learning (ML)-based method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditional root-finding techniques are computationally expensive, particularly for large-scale relativistic hydrodynamics simulations. To address this, we employ feedforward neural networks (NNC2PS and NNC2PL), trained in PyTorch (2.0+) and optimized for GPU inference using NVIDIA TensorRT (8.4.1), achieving significant speedups with minimal accuracy loss. The NNC2PS model achieves L1 and L∞ errors of 4.54×10−7 and 3.44×10−6, respectively, while the NNC2PL model exhibits even lower error values. TensorRT optimization with mixed-precision deployment substantially accelerates performance compared to traditional root-finding methods. Specifically, the mixed-precision TensorRT engine for NNC2PS achieves inference speeds approximately 400 times faster than a traditional single-threaded CPU implementation for a dataset size of 1,000,000 points. Ideal parallelization across an entire compute node in the Delta supercomputer (dual AMD 64-core 2.45 GHz Milan processors and 8 NVIDIA A100 GPUs with 40 GB HBM2 RAM and NVLink) predicts a 25-fold speedup for TensorRT over an optimally parallelized numerical method when processing 8 million data points. Moreover, the ML method exhibits sub-linear scaling with increasing dataset sizes. We release the scientific software developed, enabling further validation and extension of our findings. By exploiting the underlying symmetries within the equation of state, these findings highlight the potential of ML, combined with GPU optimization and model quantization, to accelerate conservative-to-primitive inversion in relativistic hydrodynamics simulations. Full article
(This article belongs to the Special Issue Symmetry in Gravitational Wave Physics)
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