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• Journal article
  Abbasi J, Jagtap AD, Moseley B, Hiorth A, Andersen PØet al., 2025,

  Challenges and advancements in modeling shock fronts with physics-informed neural networks: A review and benchmarking study

  , Neurocomputing, Vol: 657, ISSN: 0925-2312

  Solving partial differential equations (PDEs) with discontinuous solutions—such as shock waves in multiphase viscous flow in porous media—is critical for a wide range of scientific and engineering applications, as they represent sudden changes in physical quantities. Physics-Informed Neural Networks (PINNs), an approach proposed for solving PDEs, encounter significant challenges when applied to such systems. Accurately solving PDEs with discontinuities using PINNs requires specialized techniques to ensure effective solution accuracy and numerical stability. Various methods have been developed to address the challenges of modeling discontinuities within the PINNs framework. This work reviews and benchmarks these approaches across problems of varying complexity, categorizing them into three broad groups, influencing solution accuracy differently. (1) Physics-modification (PM) methods improve accuracy by modifying the system's physics, such as adding artificial viscosity or enforcing entropy constraints. (2) Loss and training modification (LM) techniques focus on regularizing the loss landscape, often by refining the loss term in high-error regions. (3) Architecture-modification (AM) approaches, on the other hand, propose advanced network designs to handle discontinuities better. A benchmarking study was conducted on two multiphase flow problems in porous media: the classic Buckley-Leverett (BL) problem and a fully coupled system of equations involving shock waves but with varying levels of solution complexity. The findings show that PM and LM approaches can provide accurate solutions for the BL problem by effectively addressing the infinite gradients associated with shock occurrences. In contrast, AM methods failed to effectively resolve the shock waves. When applied to fully coupled PDEs (with more complex loss landscapes), the generalization error in the solutions quickly increased, highlighting the need for ongoing innovation. This study provides a comp

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• Journal article
  Abbasi J, Moseley B, Kurotori T, Jagtap AD, Kovscek AR, Hiorth A, Andersen POet al., 2025,

  History-Matching of imbibition flow in fractured porous media Using Physics-Informed Neural Networks (PINNs)

  , COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 437, ISSN: 0045-7825
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  • Citations: 4
• Journal article
  Alsubeihi M, Jessop A, Moseley B, Fonte CP, Rajagopalan AKet al., 2025,

  Modern, Efficient, and Differentiable Transport Equation Models Using JAX: Applications to Population Balance Equations

  , INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 64, Pages: 4541-4553, ISSN: 0888-5885
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• Journal article
  Dolean V, Heinlein A, Mishra S, Moseley Bet al., 2024,

  Multilevel domain decomposition-based architectures for physics-informed neural networks

  , COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 429, ISSN: 0045-7825
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• Journal article
  Delgado-Centeno JI, Harder P, Bickel V, Moseley B, Kalaitzis F, Ganju S, Olivares-Mendez MAet al., 2024,

  Superresolution of Lunar Satellite Images for Enhanced Robotic Traverse Planning: Maximizing the Value of Existing Data Products for Space Robotics

  , IEEE ROBOTICS & AUTOMATION MAGAZINE, Vol: 31, Pages: 100-112, ISSN: 1070-9932
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• Journal article
  Moseley B, Markham A, Nissen-Meyer T, 2023,

  Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

  , ADVANCES IN COMPUTATIONAL MATHEMATICS, Vol: 49, ISSN: 1019-7168
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• Journal article
  Bickel VT, Moseley B, Hauber E, Shirley M, Williams J-P, Kring DAet al., 2022,

  Cryogeomorphic Characterization of Shadowed Regions in the Artemis Exploration Zone

  , GEOPHYSICAL RESEARCH LETTERS, Vol: 49, ISSN: 0094-8276
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• Journal article
  Szenicer A, Reinwald M, Moseley B, Nissen-Meyer T, Muteti ZM, Oduor S, McDermott-Roberts A, Baydin AG, Mortimer Bet al., 2022,

  Seismic savanna: machine learning for classifying wildlife and behaviours using ground-based vibration field recordings

  , REMOTE SENSING IN ECOLOGY AND CONSERVATION, Vol: 8, Pages: 236-250
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  • Citations: 15
• Journal article
  Bickel VT, Moseley B, Lopez-Francos I, Shirley Met al., 2021,

  Peering into lunar permanently shadowed regions with deep learning

  , NATURE COMMUNICATIONS, Vol: 12
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• Journal article
  Reinwald M, Moseley B, Szenicer A, Nissen-Meyer T, Oduor S, Vollrath F, Markham A, Mortimer Bet al., 2021,

  Seismic localization of elephant rumbles as a monitoring approach

  , JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 18, ISSN: 1742-5689
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  • Citations: 14

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