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<p>Physics-informed digital twins in computational engineering require more than a simulation model or a machine-learning surrogate. They connect a physical asset to a computational representation, learn from data, quantify uncertainty, update under sensor information, and support engineering decisions under explicit latency and credibility constraints. This scoping review synthesizes six research questions on physics-informed learning, neural operators, uncertainty quantification, data assimilation, application-domain maturity, and evidence-informed method selection. Seventy-nine database-identified scholarly reports inform the synthesis; the RQ-specific counts are 15, 14, 13, 26, 27, and 17, with overlap permitted across questions. Each headline claim is appraised through test-set construction, baseline adequacy, in- versus out-of-distribution evaluation, uncertainty and failure analysis, validation provenance, and complete sensing-to-decision latency. Numerical errors, speedups, and update times are interpreted within their study-specific evaluation settings rather than as universal selection thresholds. The evidence indicates that physics-informed neural networks can support bounded inverse, surrogate, and sparse-data tasks, but their maturity is constrained by optimization pathologies, boundary enforcement, numerical baselines, and distribution shift. Neural operators provide moderate evidence of acceleration for repeated partial differential equation prediction, but only limited evidence as complete digital-twin backbones because offline cost, geometry and regime transfer, uncertainty calibration, and operational synchronization are inconsistently reported. Uncertainty quantification and data assimilation are essential credibility and synchronization layers, yet calibration non-identifiability, misuse of information-based covariance bounds, ensemble miscalibration, filter divergence, observability limits, and incomplete end-to-end validation remain common. A non-compensatory evidence-gate procedure converts the critical synthesis into method-selection guidance while preserving negative, conflicting, and unreported evidence.</p>

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Physics-Informed Digital Twins in Computational Engineering: a PRISMA-ScR Review of Physics-Informed Learning, Neural Operators, Uncertainty Quantification, Real-Time Updating, and Deployment Readiness

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Shimon Fridkin

摘要

Physics-informed digital twins in computational engineering require more than a simulation model or a machine-learning surrogate. They connect a physical asset to a computational representation, learn from data, quantify uncertainty, update under sensor information, and support engineering decisions under explicit latency and credibility constraints. This scoping review synthesizes six research questions on physics-informed learning, neural operators, uncertainty quantification, data assimilation, application-domain maturity, and evidence-informed method selection. Seventy-nine database-identified scholarly reports inform the synthesis; the RQ-specific counts are 15, 14, 13, 26, 27, and 17, with overlap permitted across questions. Each headline claim is appraised through test-set construction, baseline adequacy, in- versus out-of-distribution evaluation, uncertainty and failure analysis, validation provenance, and complete sensing-to-decision latency. Numerical errors, speedups, and update times are interpreted within their study-specific evaluation settings rather than as universal selection thresholds. The evidence indicates that physics-informed neural networks can support bounded inverse, surrogate, and sparse-data tasks, but their maturity is constrained by optimization pathologies, boundary enforcement, numerical baselines, and distribution shift. Neural operators provide moderate evidence of acceleration for repeated partial differential equation prediction, but only limited evidence as complete digital-twin backbones because offline cost, geometry and regime transfer, uncertainty calibration, and operational synchronization are inconsistently reported. Uncertainty quantification and data assimilation are essential credibility and synchronization layers, yet calibration non-identifiability, misuse of information-based covariance bounds, ensemble miscalibration, filter divergence, observability limits, and incomplete end-to-end validation remain common. A non-compensatory evidence-gate procedure converts the critical synthesis into method-selection guidance while preserving negative, conflicting, and unreported evidence.