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Structural vibration monitoring with diffractive optical processors.

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Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Shell · 36 lines · 1.2 KB · BSD-3-Clause

  1. set -ex
  3. LOCAL_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
  4. ROOT_DIR=$(cd "$LOCAL_DIR"/../.. && pwd)
  5. TEST_DIR="$ROOT_DIR/test"
  6. gtest_reports_dir="${TEST_DIR}/test-reports/cpp"
  7. pytest_reports_dir="${TEST_DIR}/test-reports/python"
  9. # Figure out which Python to use
  10. PYTHON="$(which python)"
  11. if [[ "${BUILD_ENVIRONMENT}" =~ py((2|3)\.?[0-9]?\.?[0-9]?) ]]; then
  12. PYTHON=$(which "python${BASH_REMATCH[1]}")
  13. fi
  15. if [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
  16. # HIP_PLATFORM is auto-detected by hipcc; unset to avoid build errors
  17. unset HIP_PLATFORM
  18. if which sccache > /dev/null; then
  19. # Save sccache logs to file
  20. sccache --stop-server || true
  21. rm -f ~/sccache_error.log || true
  22. SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=0 sccache --start-server
  24. # Report sccache stats for easier debugging
  25. sccache --zero-stats
  26. fi
  27. fi
  29. # /usr/local/caffe2 is where the cpp bits are installed to in cmake-only
  30. # builds. In +python builds the cpp tests are copied to /usr/local/caffe2 so
  31. # that the test code in .ci/test.sh is the same
  32. INSTALL_PREFIX="/usr/local/caffe2"
  34. mkdir -p "$gtest_reports_dir" || true
  35. mkdir -p "$pytest_reports_dir" || true
  36. mkdir -p "$INSTALL_PREFIX" || true

common.sh at commit 35d22a1, under BSD-3-Clause · at the source

Overview

Authors: Yuntian Wang1,2,3, Zafer Yilmaz4, Yuhang Li1,2,3, Edward Liu1, Eric Ahlberg4, Farid Ghahari5, Ertugrul Taciroglu4, Aydogan Ozcan1,2,3
ORCID iDs: Yuntian Wang, Yuhang Li, Edward Liu, Farid Ghahari, Ertugrul Taciroglu, Aydogan Ozcan
  1. Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA
  2. Bioengineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA
  3. California NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, CA 90095, USA
  4. Civil and Environmental Engineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA
  5. California Geological Survey, California Department of Conservation, Sacramento, CA 95814, USA
Institutions: California NanoSystems Institute (United States); University of California, Los Angeles (United States); California Department of Conservation (United States); California Geological Survey
Journal: Science advances, volume 12, issue 10, article eaea1712
Dates: received 29 June 2025; accepted 28 January 2026; published online 4 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aea1712 · PMID 41779852 · PMCID PMC12959400 · OpenAlex W7133524081
Open access: gold, a free copy (OpenAlex)
Preprint: arxiv:2506.03317
Status: code verified
Methods: Spectral & time-frequency, Connectivity, Machine learning
Topic: Neural Networks and Reservoir Computing (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: Volgenau Chair at UCLA; V.M. Watanabe Award
Citations: cited by 1 paper (Europe PMC); 58 references in the paper

Abstract

Structural health monitoring (SHM) is vital for maintaining the safety and longevity of civil infrastructure, yet current solutions remain constrained by cost, power consumption, scalability, and the complexity of data processing. Here, we present a diffractive vibration monitoring system, integrating a jointly optimized diffractive layer with a shallow neural network-based backend to remotely extract three-dimensional (3D) structural vibration spectra, offering a low-power, cost-effective, and scalable solution. Unlike prior diffractive processors designed primarily for static image classification or reconstruction tasks, this framework establishes a dynamic computational sensing modality where the optical front-end is co-optimized to encode time-varying mechanical vibrations into distinct spatiotemporal optical patterns. This architecture eliminates the need for dense sensor arrays or extensive data acquisition; instead, it uses a spatially optimized passive diffractive layer that encodes 3D structural displacements into modulated light, captured by a minimal number of detectors and decoded in real time by shallow and low-power neural networks to reconstruct the 3D displacement spectra of structures. The diffractive system’s efficacy was demonstrated both numerically and experimentally using millimeter-wave illumination on a laboratory-scale building model with a programmable shake table. Our system achieves more than an order-of-magnitude improvement in accuracy over conventional optics or separately trained modules, establishing a foundation for high-throughput 3D monitoring of structures. Beyond SHM, the 3D vibration monitoring capabilities of this cost-effective and data-efficient framework establish a distinct computational sensing modality with potential applications in disaster resilience, aerospace diagnostics, and autonomous navigation—where energy efficiency, low latency, and high-throughput are critical.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

pytorch/pytorch

License: BSD-3-Clause
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 35d22a1a2c36bc86b5a9bfbf7e625b369a77ff51, 30 September 2026
Languages: Python (4992), C/C++ (2421), C++ (2328), CUDA (361), C (194), Shell (182), Java (21), Jupyter (11), JavaScript (5)
Size: 22,711 files, 10,515 scripts
Software Heritage: archived
Found in: “Data, code, and materials availability:”
Holds: README, license file, CITATION.cff, environment (Dockerfile, .devcontainer/Dockerfile, .devcontainer/README.md, .github/requirements-gha-cache.txt, .ci/docker/requirements-ci.txt, .ci/docker/requirements-docs.txt, .ci/lumen_cli/pyproject.toml, .devcontainer/cpu/devcontainer.json, .devcontainer/cuda/devcontainer.json, .devcontainer/cuda/requirements.txt, .devcontainer/scripts/install-dev-tools.sh, .devcontainer/scripts/update_alternatives_clang.sh), tests, continuous integration
Not found: documentation
Tools: PyTorch (15 files), NumPy (3 files), JAX (1 file), NetworkX (1 file), Numba (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
2,000 files
At the source: github.com/pytorch/pytorch

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1,998 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. The codes used in this work use standard libraries and scripts that are publicly available in PyTorch: https://github.com/pytorch/pytorch.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 2 funders, 11 references.

Cite

This paper

Wang, Y., Yilmaz, Z., Li, Y., Liu, E., Ahlberg, E., Ghahari, F., Taciroglu, E., & Ozcan, A. (2026). Structural vibration monitoring with diffractive optical processors. Science advances, 12(10), eaea1712. https://doi.org/10.1126/sciadv.aea1712

BibTeX

@article{wang2026structural,
author = {Wang, Yuntian and Yilmaz, Zafer and Li, Yuhang and Liu, Edward and Ahlberg, Eric and Ghahari, Farid and Taciroglu, Ertugrul and Ozcan, Aydogan},
title = {{Structural vibration monitoring with diffractive optical processors}},
journal = {Science advances},
year = {2026},
month = mar,
volume = {12},
number = {10},
pages = {eaea1712},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aea1712},
url = {https://doi.org/10.1126/sciadv.aea1712},
pmid = {41779852},
pmcid = {PMC12959400}
}

RIS

TY - JOUR
AU - Wang, Yuntian
AU - Yilmaz, Zafer
AU - Li, Yuhang
AU - Liu, Edward
AU - Ahlberg, Eric
AU - Ghahari, Farid
AU - Taciroglu, Ertugrul
AU - Ozcan, Aydogan
TI - Structural vibration monitoring with diffractive optical processors
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/03/04
VL - 12
IS - 10
SP - eaea1712
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aea1712
UR - https://doi.org/10.1126/sciadv.aea1712
LA - en
ER -

CSL-JSON

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}
],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "10",
"page": "eaea1712",
"DOI": "10.1126/sciadv.aea1712",
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