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• Journal article
  Norouzi S, Moldrup P, Moseley B, Robinson D, Or D, Hohenbrink TL, Minasny B, Sadeghi M, Arthur E, Tuller M, Greve MH, de Jonge LWet al., 2026,

  A differentiable hybrid modeling approach for learning soil water retention mechanisms from partial knowledge and data

  , Journal of Hydrology, Vol: 668, ISSN: 0022-1694

  Soil physics models have long relied on simplifying assumptions to represent complex processes, yet such assumptions can strongly bias model predictions. Here, we propose differentiable hybrid modeling (DHM) as a paradigm-shifting framework that learns unobservable intrinsic processes from data and physical constraints, rather than simplifying them. As a proof of concept, we apply the DHM approach to the challenge of partitioning the soil water retention curve (SWRC) into capillary and adsorbed water components, a problem where traditional assumptions have led to divergent results. The hybrid framework derives this partitioning directly from data while remaining guided by simple physical constraints. Using basic soil physical properties as inputs, the DHM couples an analytical formula for the dry end of the SWRC with data-driven physics-informed neural networks that learn the wet end, the transition between the two ends, and key soil-specific parameters. The model was trained on a SWRC dataset from 482 undisturbed soil samples, spanning a broad range of texture classes and organic carbon contents. The hybrid model successfully learned both the overall shape and the capillary and adsorbed components of the SWRC. Notably, the learned patterns were consistent with pore-scale thermodynamic saturation behavior in angular pores, without relying on explicit assumptions about soil pore geometry or its distribution. Moreover, the model revealed a distinctly nonlinear transition between capillary and adsorbed domains, challenging the linear assumptions invoked in previous studies. The methodology introduced here provides a blueprint for learning other soil processes where high-quality datasets are available but mechanistic understanding is incomplete.

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• Journal article
  van Beek JW, Dolean V, Moseley B, 2026,

  Local feature filtering for scalable and well-conditioned domain-decomposed random feature methods

  , COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 449, ISSN: 0045-7825
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• Journal article
  Abbasi J, Jagtap AD, Moseley B, Hiorth A, Andersen POet 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
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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

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