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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

  Random Feature Methods (RFMs) [1] and their variants such as extreme learning machine finite-basis physics-informed neural networks (ELM-FBPINNs) [2] offer a scalable approach for solving partial differential equations (PDEs) by using localized, overlapping and randomly initialized neural network basis functions to approximate the PDE solution and training them to minimize PDE residuals through solving structured least-squares problems. This combination leverages the approximation power of randomized neural networks, the parallelism of domain decomposition, and the accuracy and efficiency of least-squares solvers. However, the resulting structured least-squares systems are often severely ill-conditioned, due to local redundancy among random basis functions and correlation introduced by subdomain overlaps, which significantly affects the convergence of standard solvers. In this work, we introduce a block rank-revealing QR (RRQR) filtering and preconditioning strategy that operates directly on the structured least-squares problem. First, local RRQR factorizations identify and remove redundant basis functions while preserving numerically informative ones, reducing problem size, and improving conditioning. Second, we use these factorizations to construct a right preconditioner for the global problem which preserves block-sparsity and numerical stability. Third, we derive deterministic bounds of the condition number of the preconditioned system, with probabilistic refinements for small overlaps. We validate our approach on challenging, multi-scale PDE problems in 1D, 2D, and (2+1)D, demonstrating reductions in condition numbers by up to eleven orders of magnitude, LSQR convergence speedups by factors of 10–1000, and higher accuracy than both unpreconditioned and additive Schwarz-preconditioned baselines, all at significantly lower memory and computational cost. These results establish RRQR-based preconditioning as a scalable, accurate, and efficient enhancement for

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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
  • Cite
  • 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
  • Cite
  • 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
  • Cite

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