mlsanityData to 7 Oct 2026

j-Wave: An open-source differentiable wave simulator

arXiv 2207.01499 ↗First posted 30 Jun 2022physics.comp-ph cs.LG cs.MScited by 9

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Abstract (arXiv)

We present an open-source differentiable acoustic simulator, j-Wave, which can solve time-varying and time-harmonic acoustic problems. It supports automatic differentiation, which is a program transformation technique that has many applications, especially in machine learning and scientific computing. j-Wave is composed of modular components that can be easily customized and reused. At the same time, it is compatible with some of the most popular machine learning libraries, such as JAX and TensorFlow. The accuracy of the simulation results for known configurations is evaluated against the widely used k-Wave toolbox and a cohort of acoustic simulation software. j-Wave is available from https://github.com/ucl-bug/jwave.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 16 of its 52 references that are in the map. First posted on arXiv 30 Jun 2022.

Builds on

16 of 52 references.

References · 16

  1. 2018Neural Ordinary Differential Equationscited by 3,733
  2. 2019The frontier of simulation-based inferencecited by 490
  3. 2021Machine learning accelerated computational fluid dynamicscited by 461
  4. 2020Universal Differential Equations for Scientific Machine Learningcited by 345
  5. 2019DiffTaichi: Differentiable Programming for Physical Simulationcited by 248
  6. 2021Efficient and Modular Implicit Differentiationcited by 199
  7. 2020Learning to Control PDEs with Differentiable Physicscited by 131
  8. 2017Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modelingcited by 130
  9. 2019A Differentiable Programming System to Bridge Machine Learning and Scientific Computingcited by 81
  10. 2020Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learningcited by 27
  11. 2022Multigrid-augmented deep learning preconditioners for the Helmholtz equationcited by 24
  12. 2021Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulationcited by 17
4 more
  1. 2020The Koopman Expectation: An Operator Theoretic Method for Efficient Analysis and Optimization of Uncertain Hybrid Dynamical Systemscited by 5
  2. 2019Neural network augmented wave-equation simulationcited by 3
  3. 2021Stride: a flexible platform for high-performance ultrasound computed tomographycited by 3
  4. 2021A research framework for writing differentiable PDE discretizations in JAXcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 4; 2025: 1; 2026: 1.

9 papers.

  1. 2024Automatic Differentiation-based Full Waveform Inversion with Flexible Workflowscited by 4
  2. 2022A Learned Born Series for Highly-Scattering Mediacited by 3
  3. 2022Deep Variational Inverse Scatteringcited by 2
  4. 2025Physics-informed Reduced Order Modeling of Time-dependent PDEs via Differentiable Solverscited by 2
  5. 2024Diffusion assisted image reconstruction in optoacoustic tomographycited by 1
  6. 2024Gradients of Functions of Large Matricescited by 1
  7. 2023SBMLtoODEjax: Efficient Simulation and Optimization of Biological Network Models in JAXcited by 0
  8. 2024Using Spatial Diffusions for Optoacoustic Tomography Image Reconstructioncited by 0
  9. 2026A Learning-based Framework for Spatial Impulse Response Compensation in 3D Photoacoustic Computed Tomographycited by 0