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Topic
Where machine learning earns its place in CFD and FEA: surrogates, physics-informed models and design optimisation.
2 articles
Neural operators, PINNs, reduced-order models and Gaussian processes compared, then the validation machinery that makes any of them usable: design of experiments, hold-out by region, residual checks, conformal intervals and a governed loop with the solver.
When machine-learning surrogate models are worth building for CFD and FEA, how to train and validate them, and how physics-informed methods and uncertainty estimates keep them honest.
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